NET Wiki - User contributions [en]

Advanced Computer Networks (Summer 2018)

2018-07-11T10:18:06Z

Debenedetto: /* Schedule (Tentative) */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto], [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao, Sripriya Srikant Adhatarao], [http://www.net.informatik.uni-goettingen.de/people/shichang_ding, Shichang Ding], [http://www.net.informatik.uni-goettingen.de/people/fei_zhang, Fei Zhang]
|time=Thursdays, 10-12am.
|place=Rom:2.101
|univz=https://univz.uni-goettingen.de/qisserver/rds?state=wplan&act=Raum&pool=Raum&raum.rgid=8854
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing and virtualization
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Lecture slides'''
|{{Hl2}} |'''Exercise/practice slides'''
|-
| align="right" | 12.04.2018
| Course Introduction and Big Data I
| Prof. X. Fu
|
|
|-
| align="right" | 19.04.2018
| Big Data II
| Prof. X. Fu
|
|
|-
| align="right" | 26.04.2018
| NO LECTURE (Girls Day)
|
|
|
|-
| align="right" |03.05.2018
| Big Data III
| Shichang Ding
|
|
|-
| align="right" |08.05.2018
| Exercise I: Big Data
| Prof. X. Fu
|
|
|-
| align="right" | 10.05.2018
| Holiday===NO LECTURE (PUBLIC HOLIDAY)
|
|
|
|-
| align="right" | 17.05.2018
| Crowdsourcing
| X. Fu
|
|
|-
| align="right" | 24.05.2018
| Mobile Edge
| X. Fu
|
|
|-
| align="right" |28.05.2018
| Exercise II: Crowdsourcing & Mobile Edge
| Prof. X. Fu
|
|
|-
| align="right" | 31.05.2018
| Virtualization and Cloud Technologies I
| J. De Benedetto
|
|
|-
| align="right" | 07.06.2018
| Virtualization and Cloud Technologies II
| F. Zhang
|
|
|-
| align="right" |12.06.2018
| Exercise III: Docker
| F. Zhang
|
|
|-
| align="right" | 14.06.2018
| Virtualization and Cloud Technologies III
| F. Zhang
|
|
|-
| align="right" | 21.06.2018
| Virtualization and Cloud Technologies IV
| J. De Benedetto
|
|
|-
| align="right" |26.06.2018
| Exercise IV: OpenStack
| J. De Benedetto
|
|
|-
| align="right" | 28.06.2018
| Information-Centric Networks
| S. Adhatarao
|
|
|-
| align="right" |03.07.2018
| Exercise V: ICN
| S. Adhatarao
|
|
|-
| align="right" | 05.07.2018
| Information-Centric Networks
| S. Adhatarao
|
|
|-
| align="right" | 12.07.2018
| Written Examination (same time as the lecture. Room 2.101)
|
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2018)

2018-04-06T08:59:50Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=4 ECTS (BSc/MSc AI); 4 (ITIS)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=April 12, 16:00ct: Introduction Meeting
|place=IFI Building, Room 1.101
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=211342&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20181&getglobal=semester&htmlBodyOnly=true&noDBAction=y&init=y]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*There will be 2 milestones before the presentations where the students should pass before they register for the course.
**Intro milestone where the adviser make sure that the student start to work on the topic and following an accepted methodology.
**Midterm milestone. (ex. programming tasks done etc... )

*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springernature.com/cs-proceeding/llncs/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''April. 12, 16:00ct''': Introduction meeting
* TBD : Deadline for registration
* TBD : Presentations
* '''Sept. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Prerequisites'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Understanding and implementing web crawler'''
| Web crawlers collect information such the URL of the website, the meta tag information, the Web page content, the links in the webpage and the destinations leading from those links, the web page title and any other relevant information. Web crawler can be used in data mining, wherein pages are analyzed for different properties like statistics, and data analytics are then performed on them. The topic has two main tasks: 1) explore and summarize existing python-based web crawling frameworks; 2) design and implement a simple web crawler with Python, e.g., crawl some information of a simple webpage in Twitter.
| The student interested in this topic should be familiar with Python, and ideally has some practical experience with Python.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://en.wikipedia.org/wiki/Web_crawler]
|-
| '''Getting a Practical Understanding of Segment Routing'''
| Segment Routing (SR) is a new data-plane paradigm that employs source routing and software defined networking (SDN) to present an ease to manage network. The main key advantage compared to openflow based SDN is that SR embeds the path in the packet header and hence no states are kept in the core network. Your task is to understand in practical and theory how this main key affects the way SR is doing relative network tasks such as traffic enigneering and service function chaining. If you are not sure about your time schedule during this semester, please choose another topic.
| The student should be at least familiar with one programming language (eg. Java or Python), basic open-flow SDN and basic linux skills.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
| [http://www.segment-routing.net/][http://www.segment-routing.org/]
|-
| '''Hands-on study of Vector Packet Processing (VPP)'''
| The VPP platform is an extensible framework that provides out-of-the-box production quality switch/router functionality. It is the open source version of Cisco's Vector Packet Processing (VPP) technology: a high performance, packet-processing stack that can run on commodity CPUs. The benefits of this implementation of VPP are its high performance, proven technology, its modularity and flexibility, and rich feature set.
The student's task will be acquiring hands-on experience with the VPP platform (included source code). The expected outcome would be a detailed report on how to use VPP and how to create a custom plug-in (for this part a demo application should be implemented).

| Basic networking knowledge, C/C++ programming, Unix/Linux administration. Knowledge of Data Plane Development Kit (DPDK) would be beneficial (but not mandatory).
| [http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://wiki.fd.io/view/VPP]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Advanced Computer Networks (Summer 2018)

2018-03-27T10:05:34Z

Debenedetto: /* Details */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto], [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao, Sripriya Srikant Adhatarao], [http://www.net.informatik.uni-goettingen.de/people/shichang_ding, Shichang Ding], [http://www.net.informatik.uni-goettingen.de/people/fei_zhang, Fei Zhang]
|time=Thursdays, 10-12am.
|place=Rom:2.101
|univz=https://univz.uni-goettingen.de/qisserver/rds?state=wplan&act=Raum&pool=Raum&raum.rgid=8854
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing and virtualization
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 12.04.2018
| Course Introduction and Big Data I
| Prof. X. Fu
|
|-
| align="right" | 19.04.2018
| Big Data II
| Prof. X. Fu
|
|-
| align="right" | 26.04.2018
| NO LECTURE (Girls Day)
|
|
|-
| align="right" |03.05.2018
| Big Data III
| Shichang Ding
|
|-
| align="right" | 10.05.2018
| Holiday===NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 17.05.2018
| Crowdsourcing
| X. Fu
|
|-
| align="right" | 24.05.2018
| Mobile Edge
| X. Fu
|
|-
| align="right" | 31.05.2018
| Virtualization and Cloud Technologies I
| J. De Benedetto
|
|-
| align="right" | 07.06.2018
| Virtualization and Cloud Technologies II
| J. De Benedetto
|
|-
| align="right" | 14.06.2018
| Virtualization and Cloud Technologies III
| F. Zhang
|
|-
| align="right" | 21.06.2018
| Virtualization and Cloud Technologies IV
| F. Zhang
|
|-
| align="right" | 28.06.2018
| Information-Centric Networks
| S. Adhatarao
|
|-
| align="right" | 05.07.2018
| Information-Centric Networks
| S. Adhatarao
|
|-
| align="right" | 12.07.2018
| Written Examination (same time as the lecture. Room MN09, GZG)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]

Advanced Computer Networks (Summer 2018)

2018-03-27T09:21:27Z

Debenedetto: /* Details */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto], Sripriya Adhatarao
|time=Thursdays, 10-12am.
|place=Rom:2.101
|univz=https://univz.uni-goettingen.de/qisserver/rds?state=wplan&act=Raum&pool=Raum&raum.rgid=8854
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing and virtualization
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 12.04.2018
| Course Introduction and Big Data I
| Prof. X. Fu
|
|-
| align="right" | 19.04.2018
| Big Data II
| Prof. X. Fu
|
|-
| align="right" | 26.04.2018
| NO LECTURE (Girls Day)
|
|
|-
| align="right" |03.05.2018
| Big Data III
| Shichang Ding
|
|-
| align="right" | 10.05.2018
| Holiday===NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 17.05.2018
| Crowdsourcing
| X. Fu
|
|-
| align="right" | 24.05.2018
| Mobile Edge
| X. Fu
|
|-
| align="right" | 31.05.2018
| Virtualization and Cloud Technologies I
| J. De Benedetto
|
|-
| align="right" | 07.06.2018
| Virtualization and Cloud Technologies II
| J. De Benedetto
|
|-
| align="right" | 14.06.2018
| Virtualization and Cloud Technologies III
| F. Zhang
|
|-
| align="right" | 21.06.2018
| Virtualization and Cloud Technologies IV
| F. Zhang
|
|-
| align="right" | 28.06.2018
| Information-Centric Networks
| S. Adhatarao
|
|-
| align="right" | 05.07.2018
| Information-Centric Networks
| S. Adhatarao
|
|-
| align="right" | 12.07.2018
| Written Examination (same time as the lecture. Room MN09, GZG)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]

Advanced Computer Networks (Summer 2018)

2018-03-26T11:53:26Z

Debenedetto: /* Schedule (Tentative) */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= Jacopo De Benedetto, Sripriya Adhatarao
|time=Thursdays, 10-12am.
|place=Rom:2.101
|univz=https://univz.uni-goettingen.de/qisserver/rds?state=wplan&act=Raum&pool=Raum&raum.rgid=8854
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing and virtualization
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 12.04.2018
| Course Introduction and Big Data I
| Prof. X. Fu
|
|-
| align="right" | 19.04.2018
| Big Data II
| Prof. X. Fu
|
|-
| align="right" | 26.04.2018
| NO LECTURE (Girls Day)
|
|
|-
| align="right" |03.05.2018
| Big Data III
| Shichang Ding
|
|-
| align="right" | 10.05.2018
| Holiday===NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 17.05.2018
| Crowdsourcing
| X. Fu
|
|-
| align="right" | 24.05.2018
| Mobile Edge
| X. Fu
|
|-
| align="right" | 31.05.2018
| Virtualization and Cloud Technologies I
| J. De Benedetto
(Dr. M. Arumaithurai)
|
|-
| align="right" | 07.06.2018
| Virtualization and Cloud Technologies II
| J. De Benedetto
(Dr. M. Arumaithurai)
|
|-
| align="right" | 14.06.2018
| Virtualization and Cloud Technologies III
| F. Zhang
|
|-
| align="right" | 21.06.2018
| Virtualization and Cloud Technologies IV
| F. Zhang
|
|-
| align="right" | 28.06.2018
| Information-Centric Networks
| S. Adhatarao
(Dr. M. Arumaithurai)
|
|-
| align="right" | 05.07.2018
| Information-Centric Networks
| S. Adhatarao
(Dr. M. Arumaithurai)
|
|-
| align="right" | 12.07.2018
| Written Examination (same time as the lecture. Room MN09, GZG)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]

Advanced Computer Networks (Summer 2018)

2018-03-26T11:52:48Z

Debenedetto: /* Schedule (Tentative) */

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu]
|ta= Jacopo De Benedetto, Sripriya Adhatarao
|time=Thursdays, 10-12am.
|place=Rom:2.101
|univz=https://univz.uni-goettingen.de/qisserver/rds?state=wplan&act=Raum&pool=Raum&raum.rgid=8854
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing and virtualization
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 12.04.2018
| Course Introduction and Big Data I
| Prof. X. Fu
|
|-
| align="right" | 19.04.2018
| Big Data II
| Prof. X. Fu
|
|-
| align="right" | 26.04.2018
| NO LECTURE (Girls Day)
|
|
|-
| align="right" |03.05.2018
| Big Data III
| Shichang Ding
|
|-
| align="right" | 10.05.2018
| Holiday===NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 17.05.2018
| Crowdsourcing
| X. Fu
|
|-
| align="right" | 24.05.2018
| Mobile Edge
| X. Fu
|
|-
| align="right" | 31.05.2018
| Virtualization and Cloud Technologies I
| J. De Benedetto
| (Dr. M. Arumaithurai)
|
|-
| align="right" | 07.06.2018
| Virtualization and Cloud Technologies II
| J. De Benedetto
| (Dr. M. Arumaithurai)
|
|-
| align="right" | 14.06.2018
| Virtualization and Cloud Technologies III
| F. Zhang
|
|-
| align="right" | 21.06.2018
| Virtualization and Cloud Technologies IV
| F. Zhang
|
|-
| align="right" | 28.06.2018
| Information-Centric Networks
| S. Adhatarao
| (Dr. M. Arumaithurai)
|
|-
| align="right" | 05.07.2018
| Information-Centric Networks
| S. Adhatarao
| (Dr. M. Arumaithurai)
|
|-
| align="right" | 12.07.2018
| Written Examination (same time as the lecture. Room MN09, GZG)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]

Advanced Computer Networks (Summer 2018)

2018-03-15T14:05:58Z

Debenedetto: /* Schedule (Tentative) */

Sdn-exercises-mayutan

2017-12-05T10:37:02Z

Debenedetto: /* Exercise XI: Pyretic Debugging */

= Exercises =
* [https://openflow.stanford.edu/display/ONL/POX+Wiki Pox/Openflow Tutorial]
* What to Submit: source code along with screenshots for those who are not exempted

== General Hints ==
* Use the following option to get more debug info while using pox
$ ./pox/pox.py log.level --DEBUG misc.of_tutorial
** NOTE: There are two "-" (i.e. --) used for options in mininet/pox. In the wiki, sometimes the two lines
join up and show as one line.
** Best not to use the command prompt within the VM, but to use terminals created by ssh (e.g. via Putty)
*** e.g. xterm h1 doesn't work
*** there were also issues while starting pyretic

== 12 Oct ==

=== Lab IV: Data Centers ===
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_dc Exercise_DC]

== 12 Oct ==
=== Lab V: Load balancers ===
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_lb Exercise_LB]

== 13 October ==
=== Lab VI: Firewall ===
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_firewall Exercise_Firewall]

== 13 October ==
* Get the Image from Mayutan/peers

==== LAB VII: Pyretic Debugging ====
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_pyretic_debugging Exercise_Pyretic_Debugging]

== Optional ==
=== Exercise: Pyretic Firewall ===
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_pyretic_firewall Exercise_Pyretic_Firewall]

=== Exercise: Kinetic firewall (Optional) ===
* Note that -l in the instructions (e.g. infected -1) is not a numeric "1", but the small version of "L"
* Kinetic Firewall [https://dl.dropboxusercontent.com/u/1652374/SDN_Course/Exercises/kinetic.pdf Instructions], [https://dl.dropboxusercontent.com/u/1652374/SDN_Course/Exercises/kinetic_gardenwall.py Starting_Code]

==== Exercise: Kinetic like firewall using pox (optional) ====
[https://wiki.net.informatik.uni-goettingen.de/w/index.php?title=exercises_kinetic_pox_firewall Exercise_Kinetic_Pox_Firewall]

Seminar on Internet Technologies (Winter 2017/2018)

2017-11-10T11:43:30Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''Jan. 11''' : Deadline for registration
* '''Jan. 18 and Jan. 19''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (assigned to Hannah Rauterberg; partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms (assigned)'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques (Assigned to Yifan Chen)'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning (Assigned to Oleh Astappiev)'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges (assigned to Tetiana Tolmachova)'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey (assigned to Albert Demba )'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic (assigned to iman alobaidi) '''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs (Assigned to Waqar Alamgir)'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning (Assigned to Fawad Abbasi)'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data (Assigned to Muhammad Jawad)'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
| '''Towards SDN and NFV Fault Management and High Availability (assigned to Hesham Hosney) '''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives (assigned to Gulzaib Amjad)'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets (assigned to Dia Adden)'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey (assigned to Cheng Chang) '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey assigned to Rifat Rahman'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey assigned to Shruthi Shetty'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming ''' (Assigned to: Muhammad Salman Gurmani)
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services''' (Assigned to: hamid reza Karimian)
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''360-degree Videos & Virtual Reality''' (Assigned to: Masih Ghaderi)
360-degree videos are video recordings where a view in every direction is recorded at the same time, shot using an omnidirectional camera or a collection of cameras. During playback the viewer has control of the viewing direction like a panorama. They are often associated with VR (Virtual Reality), where a person using special equipment is able to "look around" in an artificial world. This task consists in study the actual solutions and protocols that enables the transmission of 360-degree videos, highlighting the challenges related to an efficient transmission of the video stream.
'''NOTE: possiblity to extend the work for master project or thesis.
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://www.com583.com/files/Redefining%20The%20Axiom%20Of%20Story_%20The%20VR%20And%20360%20Video%20Complex%20_%20TechCrunch.pdf] [http://delivery.acm.org/10.1145/2990000/2980056/p1-qian.pdf?ip=134.76.81.35&id=2980056&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=819974159&CFTOKEN=46402817&__acm__=1508238751_aa9aa8f7a54b27ba5cfa252d87c7d5df] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823660]
|-
| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas)
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of these standards and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (This can also be a starting point for a project/thesis).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''IoT with ICN''' (Assigned to : Md Tofiqul Islam)
IoT is a growing topic of Interest but existing technologies do not support the resource constrained devices efficiently. ICN is a promising new future Internet architecture and IoT can greatly benefit by using ICN. In this topic, you will explore the existing ICN proposals for IoT and will specifically work on naming challenges in IoT with ICN.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''Crawling the Internet''' (Assigned to : Hanna Holderied)
Many services specifically including Google use crawlers to systematically browse the Internet for Indexing and other purposes. In this task you will explore the different types of crawlers that exist in the internet and what are they used for. You will perform a research on how these crawlers work and what their results are used for. This topic can also lead to a potential Master project/thesis.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
|-
|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-11-02T14:12:13Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''Jan. 11''' : Deadline for registration
* '''Jan. 18 and Jan. 19''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (assigned to Hannah Rauterberg; partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms (assigned)'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques (Assigned to Yifan Chen)'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning (Assigned to Oleh Astappiev)'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges (assigned to Tetiana Tolmachova)'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey (assigned to Albert Demba )'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic (assigned to iman alobaidi) '''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs (Assigned to Waqar Alamgir)'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning (Assigned to Fawad Abbasi)'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data (Assigned to Muhammad Jawad)'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
| '''Towards SDN and NFV Fault Management and High Availability (assigned to Hesham Hosney) '''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives (assigned to Gulzaib Amjad)'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets (assigned to Dia Adden)'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey (assigned to Cheng Chang) '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey assigned to Rifat Rahman'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey assigned to Shruthi Shetty'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming ''' (Assigned to: Muhammad Salman Gurmani)
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services''' (Assigned to: hamid reza Karimian)
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''360-degree Videos & Virtual Reality'''
360-degree videos are video recordings where a view in every direction is recorded at the same time, shot using an omnidirectional camera or a collection of cameras. During playback the viewer has control of the viewing direction like a panorama. They are often associated with VR (Virtual Reality), where a person using special equipment is able to "look around" in an artificial world. This task consists in study the actual solutions and protocols that enables the transmission of 360-degree videos, highlighting the challenges related to an efficient transmission of the video stream.
'''NOTE: possiblity to extend the work for master project or thesis.
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://www.com583.com/files/Redefining%20The%20Axiom%20Of%20Story_%20The%20VR%20And%20360%20Video%20Complex%20_%20TechCrunch.pdf] [http://delivery.acm.org/10.1145/2990000/2980056/p1-qian.pdf?ip=134.76.81.35&id=2980056&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=819974159&CFTOKEN=46402817&__acm__=1508238751_aa9aa8f7a54b27ba5cfa252d87c7d5df] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823660]
|-
| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas)
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of these standards and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (This can also be a starting point for a project/thesis).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''IoT with ICN''' (Assigned to : Md Tofiqul Islam)
IoT is a growing topic of Interest but existing technologies do not support the resource constrained devices efficiently. ICN is a promising new future Internet architecture and IoT can greatly benefit by using ICN. In this topic, you will explore the existing ICN proposals for IoT and will specifically work on naming challenges in IoT with ICN.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''Crawling the Internet''' (Assigned to : Hanna Holderied)
Many services specifically including Google use crawlers to systematically browse the Internet for Indexing and other purposes. In this task you will explore the different types of crawlers that exist in the internet and what are they used for. You will perform a research on how these crawlers work and what their results are used for. This topic can also lead to a potential Master project/thesis.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
|-
|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-24T09:17:35Z

Debenedetto: /* Schedule */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZFh6U0tEUlR3NDQ Intro] [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=1rY3jIljgeOEdg_v1gjqzZS6oUc7FL7f4LEn6AAZrP-A list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T12:48:59Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=1rY3jIljgeOEdg_v1gjqzZS6oUc7FL7f4LEn6AAZrP-A list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T12:24:28Z

Debenedetto: /* Schedule */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https:// list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T11:39:00Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https:// list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T11:37:56Z

Debenedetto: /* Schedule */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T09:04:25Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:57:13Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg list]

The text of the exercises can be found [https://drive.google.com/open?id=0B6KjNnPdhIrqdmp1ZDZWNXJkdUE here]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:54:57Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg list]

The text of the exercises can be found [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg here]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:52:49Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''
To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=0B6KjNnPdhIrqdkE0YXR3dTRIazg list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:51:13Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''
To know which exercises have you already submitted please consult this list

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:50:51Z

Debenedetto: /* Passing requirement: Earn 50% of the points on each of the exercises. */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-18T08:49:57Z

Debenedetto: /* Instructions to submit the exercises */

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

=Passing requirement: Earn 50% of the points on each of the exercises.=
To know which exercises have you already submitted please consult this list

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-17T11:41:01Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques (Assigned to Yifan Chen)'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges (assigned to Tetiana Tolmachova)'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs (Assigned to Waqar Alamgir)'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning (Assigned to Fawad Abbasi)'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets (assigned to Dia Adden)'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey (assigned to Cheng Chang) '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming ''' (Assigned to: Muhammad Salman Gurmani)
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''360-degree Videos & Virtual Reality'''
360-degree videos are video recordings where a view in every direction is recorded at the same time, shot using an omnidirectional camera or a collection of cameras. During playback the viewer has control of the viewing direction like a panorama. They are often associated with VR (Virtual Reality), where a person using special equipment is able to "look around" in an artificial world. This task consists in study the actual solutions and protocols that enables the transmission of 360-degree videos, highlighting the challenges related to an efficient transmission of the video stream.
'''NOTE: possiblity to extend the work for master project or thesis.
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://www.com583.com/files/Redefining%20The%20Axiom%20Of%20Story_%20The%20VR%20And%20360%20Video%20Complex%20_%20TechCrunch.pdf] [http://delivery.acm.org/10.1145/2990000/2980056/p1-qian.pdf?ip=134.76.81.35&id=2980056&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=819974159&CFTOKEN=46402817&__acm__=1508238751_aa9aa8f7a54b27ba5cfa252d87c7d5df] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823660]
|-
| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas)
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of these standards and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (This can also be a starting point for a project/thesis).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''IoT with ICN''' (Assigned to : Md Tofiqul Islam)
IoT is a growing topic of Interest but existing technologies do not support the resource constrained devices efficiently. ICN is a promising new future Internet architecture and IoT can greatly benefit by using ICN. In this topic, you will explore the existing ICN proposals for IoT and will specifically work on naming challenges in IoT with ICN.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''Crawling the Internet''' (Assigned to : Hanna Holderied)
Many services specifically including Google use crawlers to systematically browse the Internet for Indexing and other purposes. In this task you will explore the different types of crawlers that exist in the internet and what are they used for. You will perform a research on how these crawlers work and what their results are used for. This topic can also lead to a potential Master project/thesis.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
|-
|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Theses and Projects

2017-10-11T14:29:31Z

Debenedetto: /* Information Centric Networking (ICN) */

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project



=== Software Defined Networks (SDN) ===

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

===Network Function Virtualization (NFV) ===
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Future Internet architecture ===

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Video Delivery: Implementation and validation of SAID, a congestion control protocol for Multicast (A joint project with CISCO) ]] (B/M/P)

* [[Network Management: Information Centric Networking (ICN) based solution for Network Management]] (B/M/P)

* [[Infrastructureless, Delay Tolerant Network in different Context: Internet of Things, Emergency, Mobile Social Networks, Pervasive Computing]] (B/M/P) (currently unavailable)

* [[Disaster Recovery: Implementation and evaluation of Mobile phone based Information Centric Networking (ICN) solution for support during disasters]] (B/M/P) (currently unavailable)

* [[Wireless mesh networks/vehicular networks/wireless sensor networks: Information Centric Networking (ICN) based solution]] (B/M/P) (currently unavailable)

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Quora]] (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking ===
* '''New!''' [[Fake Account Detection in Social Networks with Machine Learning]] (M/P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll])
* '''New!''' [[Simulation of a large scale distributed Online Social Network]] (P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll])
* '''New!''' [[Optimization of Data Replication in Online Social Networks with Constraint Programming]] (P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll])
* '''New!''' [[Deploying Distributed Online Social Networks on Home Gateways]] (M/P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll]) (available from 1/7/2017)
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* '''New!''' [[Understanding answerer motivation in community Q&A]] (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])
* '''New!''' [[Community detection and analysis in community Q&A]] (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

=== Information Centric Networking (ICN) ===
* ICN over GTS: exploit Geant Testbed Service to build configurable ICN testbeds (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])
* ICNProSe: ICN-based Proximity Discovery Services (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])

== Ongoing Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Design and Implementation of a distributed OSN on Home Gateways (Student project and Master's Thesis)
|[http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
|
| Assigned to Dieter Lechler
|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
|}

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Personalized Recommender System Design (Master thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
| Build a personalized context-aware recommender system for customers according to their own interest.
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
|}

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Software-defined Networking (Winter 2017/2018)

2017-10-10T12:07:56Z

Debenedetto: /* Schedule */

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=184922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20162&idcol=k_semester.semid&idval=20162&getglobal=semester]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I
| Lab IV
| Lecture VI: Enhanced Data Plane II
| Lab V
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API
| Lab VI
| Lab VII
| Exercise for Lectures V, VI, VII
|-
|}

'''** Note: this session starts at 15:15. The subsequent exercise starts at approximately 16:00.'''

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-10T12:07:17Z

Debenedetto: /* Schedule */

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=184922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20162&idcol=k_semester.semid&idval=20162&getglobal=semester]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| Tutorial I
| [https://drive.google.com/open?id=0B6KjNnPdhIrqeWpjUHJvTktPNFE]
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I
| Lab IV
| Lecture VI: Enhanced Data Plane II
| Lab V
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API
| Lab VI
| Lab VII
| Exercise for Lectures V, VI, VII
|-
|}

'''** Note: this session starts at 15:15. The subsequent exercise starts at approximately 16:00.'''

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-09T12:07:55Z

Debenedetto: /* Details */

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=184922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20162&idcol=k_semester.semid&idval=20162&getglobal=semester]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| Lecture I: Introduction to SDN
| Lecture II: OpenFlow and its Applications
| Lecture III: Network Virtualization via SDN
| Exercise for lecture I + II
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| Lecture IV: SDN Controllers
| Exercise for lecture III + IV
| Tutorial I
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I
| Lab IV
| Lecture VI: Enhanced Data Plane II
| Lab V
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API
| Lab VI
| Lab VII
| Exercise for Lectures V, VI, VII
|-
|}

'''** Note: this session starts at 15:15. The subsequent exercise starts at approximately 16:00.'''

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-09T12:07:14Z

Debenedetto: /* Details */

{{Announcement| There are only 30 slots available, therefore register as soon as possible. In order to register, please send a subscription request here (best with your official university id and add your full name, else it is difficult for us to distinguish your request from a spam request) and wait for a week or two (we usually do batch processing, but in the order in which the requests arrived) and check if your name appears in the list of registered/waiting list participants: https://listserv.gwdg.de/mailman/listinfo/sdn-course}}

{{Announcement| See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=184922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20162&idcol=k_semester.semid&idval=20162&getglobal=semester]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| Lecture I: Introduction to SDN
| Lecture II: OpenFlow and its Applications
| Lecture III: Network Virtualization via SDN
| Exercise for lecture I + II
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| Lecture IV: SDN Controllers
| Exercise for lecture III + IV
| Tutorial I
| Lab I
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| Tutorial II
| Lab II
| Tutorial III
| Lab III
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I
| Lab IV
| Lecture VI: Enhanced Data Plane II
| Lab V
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API
| Lab VI
| Lab VII
| Exercise for Lectures V, VI, VII
|-
|}

'''** Note: this session starts at 15:15. The subsequent exercise starts at approximately 16:00.'''

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-09T11:13:03Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges '''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao and Jacopo De Benedetto
Email: adhatarao@cs.uni-goettingen.de , jacopo.de-benedetto@cs.uni-goettingen.de
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming '''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''Low-Rate Wireless Personal Area Networks'''
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of the standard and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (can also be a starting point for a project/thesis).
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-09T08:51:55Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges '''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao and Jacopo De Benedetto
Email: adhatarao@cs.uni-goettingen.de , jacopo.de-benedetto@cs.uni-goettingen.de
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming (assigned to Nikolaj Kopp)'''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''Low-Rate Wireless Personal Area Networks'''
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of the standard and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (can also be a starting point for a project/thesis).
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-09T08:47:58Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges '''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao and Jacopo De Benedetto
Email: adhatarao@cs.uni-goettingen.de , jacopo.de-benedetto@cs.uni-goettingen.de
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Low-Rate Wireless Personal Area Networks'''
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of the standard and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (can also be a starting point for a project/thesis).
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-09T08:46:34Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges '''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao and Jacopo De Benedetto
Email: adhatarao@cs.uni-goettingen.de , jacopo.de-benedetto@cs.uni-goettingen.de
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Low-Rate Wireless Personal Area Networks'''
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of the standard and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (can also be a starting point for a project/thesis).
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto] Jacopo De Benedetto]
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html]
| [https://datatracker.ietf.org/wg/6lowpan/documents/]
| [https://www.lora-alliance.org/]
| [http://www.zigbee.org/]
| [http://threadgroup.org]
|-

|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Summer 2017)

2017-04-13T11:16:16Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Dr. Hong Huang]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Apr 20, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor. The topic advisor has the right to decide whether a student is eligible for the final presentation or not according to their communication.
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Apr. 20, 16:00ct''': Introduction meeting
* '''TBA''' : Deadline for registration
* '''TBA''' : Presentations
* '''September. 30, 2017, 23:59''': Deadline for submission of report (should be sent to the topic advisor!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
|-
| '''Deep into Google Translate (assigned to Monisha Khurana)'''
This study is to provide a comprehensive study of one of the Google products - Google translate and aim to understand the technologies behind it.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [https://research.googleblog.com/2016/09/a-neural-network-for-machine.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue]
|-
| '''Inferring social capital from big data'''
This study is to discover the state of art of social capital measuring, particularly, from big data perspective.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [http://science.sciencemag.org/content/350/6264/1073][http://www.sciencedirect.com/science/article/pii/S0378873314000033]
|-
|'''An overview on deep learning framework'''
In this work, you will be asked to do a survey on all popular deep learning framework either in academe or industry, like tensorflow, caffe and so on. You shall elaborate their shortcomings and advantages.
|[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
|[https://deeplearning4j.org/compare-dl4j-torch7-pylearn]
|-
| '''Industrie 4.0: Networking prospective and challenges'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Bitcoin: state of the art and position paper (Assigned to Amine Lasfar)'''
This study is to provide a comprehensive study of the current situation on Bitcoin. Latest advances in its structure, security and furture.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.cryptovest.co.uk/resources/Bitcoin%20paper%20Original.pdf][https://www.usenix.org/system/files/login/articles/03_meiklejohn-online.pdf]
|-
| '''Legacy devices support in SDN controllers'''
'''NOTE:''' This topic could be a good entry for master project and thesis later.
Supporting legacy network is an active research area in SDN. You should survey all techniques used up to date to solve this problem. Details may be provided later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|a good start from [https://www.usenix.org/system/files/conference/atc14/atc14-paper-levin.pdf][http://dl.acm.org/authorize?N71377]
|-
| '''Google QUIC'''
QUIC is an experimental transport layer network protocol designed by Jim Roskind at Google, initially implemented in 2012. Investigate QUIC in detail and conduct some simple experiments to compare QUIC with TCP. The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[https://en.wikipedia.org/wiki/QUIC][https://docs.google.com/document/d/1RNHkx_VvKWyWg6Lr8SZ-saqsQx7rFV-ev2jRFUoVD34/edit][https://datatracker.ietf.org/wg/quic/about/][https://github.com/google/proto-quic][https://groups.google.com/a/chromium.org/forum/#!topic/proto-quic/CioG51ecKB8]
|-
| '''Google TCP BBR'''
TCP BBR is developed by Google. Investigate BBR in detail and conduct some simple experiments to compare BBR with TCP Cubic.The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[http://queue.acm.org/detail.cfm?id=3022184][https://github.com/google/bbr][https://groups.google.com/forum/#!forum/bbr-dev]
|-
| '''Commercial usage of Multipath TCP'''
MultiPath TCP (MPTCP) is an emerging extension for TCP and it is under discussion in IETF now. Study MPTCP protocol including architecture, data transmission, default congestion control, etc. Investigate how MPTCP is used in companies.
| [https://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
| [https://tools.ietf.org/html/rfc6824][http://link.springer.com/chapter/10.1007%2F978-3-642-20757-0_35][https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/raiciu][http://dl.acm.org/citation.cfm?id=2342476][http://dl.acm.org/citation.cfm?id=2631977][https://www.usenix.org/legacy/event/nsdi11/tech/full_papers/Wischik.pdf][http://blog.multipath-tcp.org/blog/html/2015/12/25/commercial_usage_of_multipath_tcp.html]
|-
| '''Traffic Data Analysis'''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Robo advisors and AI'''
A robo-advisor (robo-adviser) is an online wealth management service that provides automated, algorithm-based portfolio management advice without the use of human financial planners. Robo-advisor is one of new examples which show how AI begin to take place of human beings in high-end service like finance, laws, education and even research. Beside gaining basic knowledge about AI, it is also a good way to understand how AI change our future work markets.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://onlinepresent.org/proceedings/vol141_2016/21.pdf]
|-
| '''Deep Learning and Alphago(Master)'''
Alphago is one of the best players in board games. One of the important reasons for its great success is deep learning. Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is now broadly studied and used in following areas: Automatic speech recognition, Image recognition, Natural language processing, Customer relationship management and so on. Alphago (its upgraded version called Master) is one of the most famous and successful applications of deep learning. It is a good way to gain knowledge about this interesting area.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://web.iitd.ac.in/~sumeet/Silver16.pdf][http://www.ieee-jas.org/CN/article/downloadArticleFile.do?attachType=PDF&id=145]
|-

|-
| '''Large-Scale Mobile Traffic Analysis - A Survey'''
This study is to provide a comprehensive study of large-scale mobile traffic analysis. You should survey the related work about this topic and focus on some classical research work. You also need to give your own opinion on the topic.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://perso.citi-lab.fr/mfiore/data/naboulsi_comst15.pdf]

|-
| '''Understanding and modelling individual human mobility'''
This study is to provide a comprehensive study of understanding and modelling individual human mobility. You should survey the related work about this topic and focus on some classical research work. You also need to give your own opinion on the topic.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| Take a look at related papers in well known conferences/workshops/journals, e.g., [http://www.ccsb.dfci.harvard.edu/web/export/sites/default/ccsb/publications/papers/2010/Song--Barabasi_NatPhysics_10.pdf]

|-
| '''Recommendations in Location-based Social Networks - A Survey'''
This study is to provide a comprehensive study of recommendations in Location-based Social Networks. You should survey the related work about this topic and focus on some classical research work. You also need to give your own opinion on the topic.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://www.microsoft.com/en-us/research/publication/recommendations-in-location-based-social-networks-a-survey/]

|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao (sripriya-srikant.adhatarao@informatik.uni-goettingen.de)
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]

|-

|-
|'''NFV Frameworks for deployment of Middleboxes and Network Functions in Telco/ISP/Data Center Networks - A Survey'''

Focus of this topic is to present a comprehensive study of the Industry and academic works targeted towards deployment of NFV in Telecommunications, Data Center and Enterprise networks. Understand and Analyze the key aspects of the predominant NFV frameworks, and characterize them in terms of the adopted standards, resource requirements, deployment factors and constraints, performance metrics, support for service function chaining, etc.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://www.opnfv.org/] [https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-martins.pdf] [http://dl.acm.org/ft_gateway.cfm?id=2940155&ftid=1754642&dwn=1&CFID=919487200&CFTOKEN=31286219] [http://superfluidity.eu/about/research-description] [https://fd.io/]

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
|'''NFV state-of-the-art and Future trends - A survey '''
Study and Understand Network Function Virtualisation (NFV), the real world use cases and deployment trends of NFV in the Datacenter, telecommunication, private networks. Survey on the reports by standardisation committees and open workgroups like IEFT/ETSI/OPNFV, primarily the specification and requirements for the NFV, and the NFV deployment models. Compare with the available open-source/commercial products if any in the market and make the study of NFV characteristics, the Key Performance Index(KPIs) for NFV and identify the open issues and challenges towards adopting to NFV. Student can choose to carry out either breadth or in-depth on particular aspect of NFV.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org] [https://www.sdxcentral.com/reports/nfv-vnf-2016/vnf/] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7350211]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|-
| '''Learning from Imbalanced Data'''
When building and training classifiers for classification problems, one commonly encountered problem is that of imbalanced data. For instance, in the case of a binary classifier, this means that one class is hugely overrepresented in the data available. Training classifiers for this kind of datasets has been a problem for some time. In this work, your task is to i) precisely introduce the imbalanced data problem, ii) discuss the state of the art of approaches for mitigating this problem (both from the perspective of learning algorithms and data manipulation techniques) and iii) find out what issues still remain open until today. Note that this topic requires a background in data science, and in particular in classification algorithms. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5128907&tag=1]
|-
| '''Deep Learning and its (possible) flaws''' (assigned to Sven Voigt)
One recent trend in machine learning is 'deep learning', where neural networks are employed for solving a wide range of problems. One prominent example of such problems is image classification. While neural networks are in fact delivering sometimes great results, they may also have some weak spots. In this work, your task is to i) make yourself familiar with neural networks, ii) discuss the state-of-the-art in image classification, and iii) to investigate some possible flaws in neural networks. Note that for this topic a background in data science, and in particular in classification algorithms, is strongly recommended. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://arxiv.org/abs/1404.7828]
|-
| '''How do self-driving cars work?'''
The topic title is pretty self-explanatory :)
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://cs.stanford.edu/people/teichman/papers/iv2011.pdf]
|-
| '''Multicast Video Streaming'''
In network communication, the transmission of information to multiple recipients can greatly benefits from multicast technology in terms of bandwidth efficiency. In particular, video streaming and downloads are beginning to take a larger share of bandwidth and will probably grow to more than 80% of all consumer Internet traffic by 2020. Although multicast transmission can easily resolve the bandwidth limitations, several issues persist, like flow and congestion control. The study involves analyzing and comparing the different solutions proposed in both research and industry.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [https://pdfs.semanticscholar.org/e682/85544e01b2075d8d5fe65569232a3de840cc.pdf] [http://web.cs.ucla.edu/classes/fall03/cs218/paper/pgmcc.pdf] [http://conferences2.sigcomm.org/acm-icn/2016/proceedings/p11-chen.pdf]
|-
| '''Adaptive Video Streaming (assigned to Nikolaj Kopp)'''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''Microsoft Natick'''
Natick is Microsoft research project to manufacture and operate an underwater datacenter. The goal of this topic is study the impact of underwater datacenters on environment and performance compared to modular datacenters.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://natick.research.microsoft.com/]
|-
| '''Big Data Optimization Algorithms'''
Big data is a current buzz word in both industry and academia. The goal of this topic is to study atleast two convex optimization based big data optimizations like firts-order, randomization, etc.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://arxiv.org/pdf/1411.0972.pdf]
|-
| '''Prediction Markets'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Open IoT platforms?'''
Take a look at the open IoT platforms and provde a summary of the differences, similarities and vision.
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
| Take a look at Fiware, Sophia, Crystal and other open platforms
|-
| '''Edge computing for IoT?'''
A study of the various edge computing solutions that exist for IoT
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
| Take a look at Amazon Lambda, Amazon IoT, Amazon greengrass and solutions by other companies
|-
| '''Service Oriented Networking?'''
The aim of this work is to take a look at the the different service oriented Networking visions and study their similarities, differences, pros and cons
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
| Take a look at recent papers in well known conferences/workshops.
|-

Theses and Projects

2017-04-13T11:11:34Z

Debenedetto: /* Information Centric Networking (ICN) */

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project



=== Software Defined Networks (SDN) ===

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

===Network Function Virtualization (NFV) ===
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Future Internet architecture ===

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Video Delivery: Implementation and validation of SAID, a congestion control protocol for Multicast (A joint project with CISCO) ]] (B/M/P)

* [[Network Management: Information Centric Networking (ICN) based solution for Network Management]] (B/M/P)

* [[Infrastructureless, Delay Tolerant Network in different Context: Internet of Things, Emergency, Mobile Social Networks, Pervasive Computing]] (B/M/P) (currently unavailable)

* [[Disaster Recovery: Implementation and evaluation of Mobile phone based Information Centric Networking (ICN) solution for support during disasters]] (B/M/P) (currently unavailable)

* [[Wireless mesh networks/vehicular networks/wireless sensor networks: Information Centric Networking (ICN) based solution]] (B/M/P) (currently unavailable)

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking ===
* [[An Analysis of OSN based Sybil Defenses]] (M/P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll]) (currently unavailable)
* [[Implementing Distributed Online Social Networks on Home Gateways]] (M/P) ([http://user.informatik.uni-goettingen.de/~dkoll David Koll]) (currently unavailable)
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* Understanding answerer motivation in community Q&A (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])
* Community detection and analysis in community Q&A (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

=== Information Centric Networking (ICN) ===
* ICN over GTS: exploit Geant Testbed Service to build configurable ICN testbeds (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])
* ICNProSe: ICN-based Proximity Discovery Services (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto]) ('''currently unavailable''')

== Ongoing Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
|}

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Personalized Recommender System Design (Master thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
| Build a personalized context-aware recommender system for customers according to their own interest.
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
|}

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Seminar on Internet Technologies (Summer 2017)

2017-04-03T14:40:11Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Dr. Hong Huang]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Apr 20, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor. The topic advisor has the right to decide whether a student is eligible for the final presentation or not according to their communication.
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Apr. 20, 16:00ct''': Introduction meeting
* '''TBA''' : Deadline for registration
* '''TBA''' : Presentations
* '''September. 30, 2017, 23:59''': Deadline for submission of report (should be sent to the topic advisor!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
|-
| '''Deep into Google Translate'''
This study is to provide a comprehensive study of one of the Google products - Google translate and aim to understand the technologies behind it.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [https://research.googleblog.com/2016/09/a-neural-network-for-machine.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue]
|-
| '''Inferring social capital from big data'''
This study is to discover the state of art of social capital measuring, particularly, from big data perspective.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [http://science.sciencemag.org/content/350/6264/1073][http://www.sciencedirect.com/science/article/pii/S0378873314000033]
|-
|'''An overview on deep learning framework'''
In this work, you will be asked to do a survey on all popular deep learning framework either in academe or industry, like tensorflow, caffe and so on. You shall elaborate their shortcomings and advantages.
|[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
|[https://deeplearning4j.org/compare-dl4j-torch7-pylearn]
|-
| '''Industrie 4.0: Networking prospective and challenges'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Bitcoin: state of the art and position paper (Assigned to Amine Lasfar)'''
This study is to provide a comprehensive study of the current situation on Bitcoin. Latest advances in its structure, security and furture.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.cryptovest.co.uk/resources/Bitcoin%20paper%20Original.pdf][https://www.usenix.org/system/files/login/articles/03_meiklejohn-online.pdf]
|-
| '''Legacy devices support in SDN controllers'''
'''NOTE:''' This topic could be a good entry for master project and thesis later.
Supporting legacy network is an active research area in SDN. You should survey all techniques used up to date to solve this problem. Details may be provided later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|a good start from [https://www.usenix.org/system/files/conference/atc14/atc14-paper-levin.pdf][http://dl.acm.org/authorize?N71377]
|-
| '''Google QUIC'''
QUIC is an experimental transport layer network protocol designed by Jim Roskind at Google, initially implemented in 2012. Investigate QUIC in detail and conduct some simple experiments to compare QUIC with TCP. The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[https://en.wikipedia.org/wiki/QUIC][https://docs.google.com/document/d/1RNHkx_VvKWyWg6Lr8SZ-saqsQx7rFV-ev2jRFUoVD34/edit][https://datatracker.ietf.org/wg/quic/about/][https://github.com/google/proto-quic][https://groups.google.com/a/chromium.org/forum/#!topic/proto-quic/CioG51ecKB8]
|-
| '''Google TCP BBR'''
TCP BBR is developed by Google. Investigate BBR in detail and conduct some simple experiments to compare BBR with TCP Cubic.The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[http://queue.acm.org/detail.cfm?id=3022184][https://github.com/google/bbr][https://groups.google.com/forum/#!forum/bbr-dev]
|-
| '''Commercial usage of Multipath TCP'''
MultiPath TCP (MPTCP) is an emerging extension for TCP and it is under discussion in IETF now. Study MPTCP protocol including architecture, data transmission, default congestion control, etc. Investigate how MPTCP is used in companies.
| [https://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
| [https://tools.ietf.org/html/rfc6824][http://link.springer.com/chapter/10.1007%2F978-3-642-20757-0_35][https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/raiciu][http://dl.acm.org/citation.cfm?id=2342476][http://dl.acm.org/citation.cfm?id=2631977][https://www.usenix.org/legacy/event/nsdi11/tech/full_papers/Wischik.pdf][http://blog.multipath-tcp.org/blog/html/2015/12/25/commercial_usage_of_multipath_tcp.html]
|-
| '''Traffic Data Analysis'''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Robo advisors and AI'''
A robo-advisor (robo-adviser) is an online wealth management service that provides automated, algorithm-based portfolio management advice without the use of human financial planners. Robo-advisor is one of new examples which show how AI begin to take place of human beings in high-end service like finance, laws, education and even research. Beside gaining basic knowledge about AI, it is also a good way to understand how AI change our future work markets.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://onlinepresent.org/proceedings/vol141_2016/21.pdf]
|-
| '''Deep Learning and Alphago(Master)'''
Alphago is one of the best players in board games. One of the important reasons for its great success is deep learning. Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is now broadly studied and used in following areas: Automatic speech recognition, Image recognition, Natural language processing, Customer relationship management and so on. Alphago (its upgraded version called Master) is one of the most famous and successful applications of deep learning. It is a good way to gain knowledge about this interesting area.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://web.iitd.ac.in/~sumeet/Silver16.pdf][http://www.ieee-jas.org/CN/article/downloadArticleFile.do?attachType=PDF&id=145]
|-

|-
| '''Mobile network data for public health - A Survey'''
This study is to provide a comprehensive study of Mobile network data for public health.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://journal.frontiersin.org/article/10.3389/fpubh.2015.00189/full]

|-
| '''Understanding and modelling individual human mobility'''
This study is to provide a comprehensive study of understanding and modelling individual human mobility.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| Take a look at related papers in well known conferences/workshops/journals, e.g., [http://www.ccsb.dfci.harvard.edu/web/export/sites/default/ccsb/publications/papers/2010/Song--Barabasi_NatPhysics_10.pdf]

|-
| '''Recommendations in Location-based Social Networks - A Survey'''
This study is to provide a comprehensive study of recommendations in Location-based Social Networks.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://www.microsoft.com/en-us/research/publication/recommendations-in-location-based-social-networks-a-survey/]

|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao (sripriya-srikant.adhatarao@informatik.uni-goettingen.de)
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]

|-

|-
|'''NFV Frameworks for deployment of Middleboxes and Network Functions in Telco/ISP/Data Center Networks - A Survey'''

Focus of this topic is to present a comprehensive study of the Industry and academic works targeted towards deployment of NFV in Telecommunications, Data Center and Enterprise networks. Understand and Analyze the key aspects of the predominant NFV frameworks, and characterize them in terms of the adopted standards, resource requirements, deployment factors and constraints, performance metrics, support for service function chaining, etc.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://www.opnfv.org/] [https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-martins.pdf] [http://dl.acm.org/ft_gateway.cfm?id=2940155&ftid=1754642&dwn=1&CFID=919487200&CFTOKEN=31286219] [http://superfluidity.eu/about/research-description] [https://fd.io/]

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
|'''NFV state-of-the-art and Future trends - A survey '''
Study and Understand Network Function Virtualisation (NFV), the real world use cases and deployment trends of NFV in the Datacenter, telecommunication, private networks. Survey on the reports by standardisation committees and open workgroups like IEFT/ETSI/OPNFV, primarily the specification and requirements for the NFV, and the NFV deployment models. Compare with the available open-source/commercial products if any in the market and make the study of NFV characteristics, the Key Performance Index(KPIs) for NFV and identify the open issues and challenges towards adopting to NFV. Student can choose to carry out either breadth or in-depth on particular aspect of NFV.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org] [https://www.sdxcentral.com/reports/nfv-vnf-2016/vnf/] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7350211]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|-
| '''Learning from Imbalanced Data'''
When building and training classifiers for classification problems, one commonly encountered problem is that of imbalanced data. For instance, in the case of a binary classifier, this means that one class is hugely overrepresented in the data available. Training classifiers for this kind of datasets has been a problem for some time. In this work, your task is to i) precisely introduce the imbalanced data problem, ii) discuss the state of the art of approaches for mitigating this problem (both from the perspective of learning algorithms and data manipulation techniques) and iii) find out what issues still remain open until today. Note that this topic requires a background in data science, and in particular in classification algorithms. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5128907&tag=1]
|-
| '''Deep Learning and its (possible) flaws'''
One recent trend in machine learning is 'deep learning', where neural networks are employed for solving a wide range of problems. One prominent example of such problems is image classification. While neural networks are in fact delivering sometimes great results, they may also have some weak spots. In this work, your task is to i) make yourself familiar with neural networks, ii) discuss the state-of-the-art in image classification, and iii) to investigate some possible flaws in neural networks. Note that for this topic a background in data science, and in particular in classification algorithms, is strongly recommended. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://arxiv.org/abs/1404.7828]
|-
| '''How do self-driving cars work?'''
The topic title is pretty self-explanatory :)
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://cs.stanford.edu/people/teichman/papers/iv2011.pdf]
|-
| '''Multicast Video Streaming'''
In network communication, the transmission of information to multiple recipients can greatly benefits from multicast technology in terms of bandwidth efficiency. In particular, video streaming and downloads are beginning to take a larger share of bandwidth and will probably grow to more than 80% of all consumer Internet traffic by 2020. Although multicast transmission can easily resolve the bandwidth limitations, several issues persist, like flow and congestion control. The study involves analyzing and comparing the different solutions proposed in both research and industry.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [https://pdfs.semanticscholar.org/e682/85544e01b2075d8d5fe65569232a3de840cc.pdf] [http://web.cs.ucla.edu/classes/fall03/cs218/paper/pgmcc.pdf] [http://conferences2.sigcomm.org/acm-icn/2016/proceedings/p11-chen.pdf]
|-
| '''Adaptive Video Streaming'''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-

Seminar on Internet Technologies (Summer 2017)

2017-04-03T14:38:29Z

Debenedetto: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Dr. Hong Huang]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Apr 20, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor. The topic advisor has the right to decide whether a student is eligible for the final presentation or not according to their communication.
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Apr. 20, 16:00ct''': Introduction meeting
* '''TBA''' : Deadline for registration
* '''TBA''' : Presentations
* '''September. 30, 2017, 23:59''': Deadline for submission of report (should be sent to the topic advisor!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
|-
| '''Deep into Google Translate'''
This study is to provide a comprehensive study of one of the Google products - Google translate and aim to understand the technologies behind it.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [https://research.googleblog.com/2016/09/a-neural-network-for-machine.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue]
|-
| '''Inferring social capital from big data'''
This study is to discover the state of art of social capital measuring, particularly, from big data perspective.
| [http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
| [http://science.sciencemag.org/content/350/6264/1073][http://www.sciencedirect.com/science/article/pii/S0378873314000033]
|-
|'''An overview on deep learning framework'''
In this work, you will be asked to do a survey on all popular deep learning framework either in academe or industry, like tensorflow, caffe and so on. You shall elaborate their shortcomings and advantages.
|[http://www.net.informatik.uni-goettingen.de/people/Hong_Huang Hong Huang]
|[https://deeplearning4j.org/compare-dl4j-torch7-pylearn]
|-
| '''Industrie 4.0: Networking prospective and challenges'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Bitcoin: state of the art and position paper (Assigned to Amine Lasfar)'''
This study is to provide a comprehensive study of the current situation on Bitcoin. Latest advances in its structure, security and furture.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.cryptovest.co.uk/resources/Bitcoin%20paper%20Original.pdf][https://www.usenix.org/system/files/login/articles/03_meiklejohn-online.pdf]
|-
| '''Legacy devices support in SDN controllers'''
'''NOTE:''' This topic could be a good entry for master project and thesis later.
Supporting legacy network is an active research area in SDN. You should survey all techniques used up to date to solve this problem. Details may be provided later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|a good start from [https://www.usenix.org/system/files/conference/atc14/atc14-paper-levin.pdf][http://dl.acm.org/authorize?N71377]
|-
| '''Google QUIC'''
QUIC is an experimental transport layer network protocol designed by Jim Roskind at Google, initially implemented in 2012. Investigate QUIC in detail and conduct some simple experiments to compare QUIC with TCP. The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[https://en.wikipedia.org/wiki/QUIC][https://docs.google.com/document/d/1RNHkx_VvKWyWg6Lr8SZ-saqsQx7rFV-ev2jRFUoVD34/edit][https://datatracker.ietf.org/wg/quic/about/][https://github.com/google/proto-quic][https://groups.google.com/a/chromium.org/forum/#!topic/proto-quic/CioG51ecKB8]
|-
| '''Google TCP BBR'''
TCP BBR is developed by Google. Investigate BBR in detail and conduct some simple experiments to compare BBR with TCP Cubic.The experiments should be designed by the student himself/herself.
| [http://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
|[http://queue.acm.org/detail.cfm?id=3022184][https://github.com/google/bbr][https://groups.google.com/forum/#!forum/bbr-dev]
|-
| '''Commercial usage of Multipath TCP'''
MultiPath TCP (MPTCP) is an emerging extension for TCP and it is under discussion in IETF now. Study MPTCP protocol including architecture, data transmission, default congestion control, etc. Investigate how MPTCP is used in companies.
| [https://www.net.informatik.uni-goettingen.de/people/enhuan_dong Enhuan Dong]
| [https://tools.ietf.org/html/rfc6824][http://link.springer.com/chapter/10.1007%2F978-3-642-20757-0_35][https://www.usenix.org/conference/nsdi12/technical-sessions/presentation/raiciu][http://dl.acm.org/citation.cfm?id=2342476][http://dl.acm.org/citation.cfm?id=2631977][https://www.usenix.org/legacy/event/nsdi11/tech/full_papers/Wischik.pdf][http://blog.multipath-tcp.org/blog/html/2015/12/25/commercial_usage_of_multipath_tcp.html]
|-
| '''Traffic Data Analysis'''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Robo advisors and AI'''
A robo-advisor (robo-adviser) is an online wealth management service that provides automated, algorithm-based portfolio management advice without the use of human financial planners. Robo-advisor is one of new examples which show how AI begin to take place of human beings in high-end service like finance, laws, education and even research. Beside gaining basic knowledge about AI, it is also a good way to understand how AI change our future work markets.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://onlinepresent.org/proceedings/vol141_2016/21.pdf]
|-
| '''Deep Learning and Alphago(Master)'''
Alphago is one of the best players in board games. One of the important reasons for its great success is deep learning. Deep learning is a class of machine learning algorithms that use a cascade of many layers of nonlinear processing units for feature extraction and transformation. It is now broadly studied and used in following areas: Automatic speech recognition, Image recognition, Natural language processing, Customer relationship management and so on. Alphago (its upgraded version called Master) is one of the most famous and successful applications of deep learning. It is a good way to gain knowledge about this interesting area.
| [https://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding]
| [http://web.iitd.ac.in/~sumeet/Silver16.pdf][http://www.ieee-jas.org/CN/article/downloadArticleFile.do?attachType=PDF&id=145]
|-

|-
| '''Mobile network data for public health - A Survey'''
This study is to provide a comprehensive study of Mobile network data for public health.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://journal.frontiersin.org/article/10.3389/fpubh.2015.00189/full]

|-
| '''Understanding and modelling individual human mobility'''
This study is to provide a comprehensive study of understanding and modelling individual human mobility.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| Take a look at related papers in well known conferences/workshops/journals, e.g., [http://www.ccsb.dfci.harvard.edu/web/export/sites/default/ccsb/publications/papers/2010/Song--Barabasi_NatPhysics_10.pdf]

|-
| '''Recommendations in Location-based Social Networks - A Survey'''
This study is to provide a comprehensive study of recommendations in Location-based Social Networks.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://www.microsoft.com/en-us/research/publication/recommendations-in-location-based-social-networks-a-survey/]

|-
|'''ICN - Information Centric Networking'''

Content Centric Networking (CCN) is a new ambitious proposal to replace the IP protocol. A better and faster content distribution, improved privacy, integrated cryptography and easy P2P communication are among the key elements of this architecture. On the other hand problems like efficiency and scalability of the name-based routing, support of existing application and new ones and the possibility to actually deploy this technology are still open and actively discussed, making CCN one of the most active research field in networking.

By choosing this topic you will gain a general knowledge of the many architecture proposed for ICN and will have to gain insight into one of the problems like routing or security, or solutions (i.e. applications on top of NDN).

- '''topics available''': Routing in ICN, IoT with ICN, ICN Architectures
- [http://named-data.net/wp-content/uploads/2013/10/ndn-annualreport2012-2013.pdf NDN technical report]
- [http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]
| Sripriya Adhatarao (sripriya-srikant.adhatarao@informatik.uni-goettingen.de)
|For general introduction:
*[http://named-data.net/a-new-way-to-look-at-networking/ Video presenting NDN]
*[http://named-data.net/wp-content/uploads/Jacob.pdf First proposal on Content Centric Networking]
*[http://tools.ietf.org/pdf/draft-pentikousis-icn-scenarios-04.pdf ICN Base line scenarios]

|-

|-
|'''NFV Frameworks for deployment of Middleboxes and Network Functions in Telco/ISP/Data Center Networks - A Survey'''

Focus of this topic is to present a comprehensive study of the Industry and academic works targeted towards deployment of NFV in Telecommunications, Data Center and Enterprise networks. Understand and Analyze the key aspects of the predominant NFV frameworks, and characterize them in terms of the adopted standards, resource requirements, deployment factors and constraints, performance metrics, support for service function chaining, etc.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://www.opnfv.org/] [https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-martins.pdf] [http://dl.acm.org/ft_gateway.cfm?id=2940155&ftid=1754642&dwn=1&CFID=919487200&CFTOKEN=31286219] [http://superfluidity.eu/about/research-description] [https://fd.io/]

|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
|'''NFV state-of-the-art and Future trends - A survey '''
Study and Understand Network Function Virtualisation (NFV), the real world use cases and deployment trends of NFV in the Datacenter, telecommunication, private networks. Survey on the reports by standardisation committees and open workgroups like IEFT/ETSI/OPNFV, primarily the specification and requirements for the NFV, and the NFV deployment models. Compare with the available open-source/commercial products if any in the market and make the study of NFV characteristics, the Key Performance Index(KPIs) for NFV and identify the open issues and challenges towards adopting to NFV. Student can choose to carry out either breadth or in-depth on particular aspect of NFV.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org] [https://www.sdxcentral.com/reports/nfv-vnf-2016/vnf/] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7350211]
|-
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|-
| '''Learning from Imbalanced Data'''
When building and training classifiers for classification problems, one commonly encountered problem is that of imbalanced data. For instance, in the case of a binary classifier, this means that one class is hugely overrepresented in the data available. Training classifiers for this kind of datasets has been a problem for some time. In this work, your task is to i) precisely introduce the imbalanced data problem, ii) discuss the state of the art of approaches for mitigating this problem (both from the perspective of learning algorithms and data manipulation techniques) and iii) find out what issues still remain open until today. Note that this topic requires a background in data science, and in particular in classification algorithms. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5128907&tag=1]
|-
| '''Deep Learning and its (possible) flaws'''
One recent trend in machine learning is 'deep learning', where neural networks are employed for solving a wide range of problems. One prominent example of such problems is image classification. While neural networks are in fact delivering sometimes great results, they may also have some weak spots. In this work, your task is to i) make yourself familiar with neural networks, ii) discuss the state-of-the-art in image classification, and iii) to investigate some possible flaws in neural networks. Note that for this topic a background in data science, and in particular in classification algorithms, is strongly recommended. Also, this topic requires a comparatively high reading effort.
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://arxiv.org/abs/1404.7828]
|-
| '''How do self-driving cars work?'''
The topic title is pretty self-explanatory :)
| [https://www.net.informatik.uni-goettingen.de/people/David_Koll David Koll ]
| [http://cs.stanford.edu/people/teichman/papers/iv2011.pdf]
|-
| '''Multicast Video Streaming'''
In network communication, the transmission of information to multiple recipients can greatly benefits from multicast technology in terms of bandwidth efficiency. In particular, video streaming and downloads are beginning to take a larger share of bandwidth and will probably grow to more than 80% of all consumer Internet traffic by 2020. Although multicast transmission can easily resolve the bandwidth limitations, several issues persist, like flow and congestion control. The study involves analyzing and comparing the different solutions proposed in both research and industry.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [https://pdfs.semanticscholar.org/e682/85544e01b2075d8d5fe65569232a3de840cc.pdf] [http://web.cs.ucla.edu/classes/fall03/cs218/paper/pgmcc.pdf] [http://conferences2.sigcomm.org/acm-icn/2016/proceedings/p11-chen.pdf]
|-
| '''Adaptive Video Streaming'''
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company name Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-

Software-defined Networking (Winder 2016/2017)

2017-02-27T10:37:27Z

Debenedetto: /* Schedule */

{{Announcement|Currently: All 30 (in fact 31) seats are taken. The rest of you will be put in a waiting list. See here for the list of currently registered participants and the members in the waiting list: https://wiki.net.informatik.uni-goettingen.de/wiki/Software-defined_Networking_Registration}}.

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=TBA
|time=22 February - 2 March 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=184922&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20162&idcol=k_semester.semid&idval=20162&getglobal=semester]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

Note: Unlike previous editions, this edition of the SDN block course will be a single course that covers both basics and more advanced concepts of SDN. The course will take one full week (i.e., 7 days) of teaching.
In addition to the lectures and practical sessions, students will be required to read and present relevant research papers in a seminar to be held after the course.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:30 - 17.00
|-
|-
|'' Theory''
| align="right" |''' 22.02.2017'''
| [[Media:lecture1.pdf | Lecture I: Introduction to SDN]]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/File:SDN_WS2015_ex_1.pdf Exercise I]
| [[Media:lecture2.pdf | Lecture II: OpenFlow and its Applications]]
| [[Media:ex2_b.pdf | Exercise II]]
|-
| ''Theory''
| align="right" | '''23.02.2017'''
| [[Media:lecture3.pdf | Lecture III: Network Virtualization via SDN]]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/File:SDN_WS2015_ex_3.pdf Exercise III]
| [[Media:lecture4_b.pdf | Lecture IV: SDN Controllers]]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/File:SDN_WS2015_ex_4.pdf Exercise IV]
|-
| ''Practical''
| align="right" | '''24.02.2017'''
| [[Media:lecture5.pdf | Tutorial I: Mininet: Basics, OpenFlow and Network Topologies]] -- [[Media:lecture_anno.pdf | Annotated Slides]] -- [http://user.informatik.uni-goettingen.de/~dkoll/files/sdn/rlab.py Rlab.py] - [http://user.informatik.uni-goettingen.de/~dkoll/files/sdn/custom_topo.py custom_topo.py]
| [[Media:ex5a.pdf | Exercise Va]] & [[Media:ex5b.pdf | Exercise Vb]]
| [[Media:lecture5.pdf | Tutorial II: Mininet and Controllers]]
| [[Media:ex6.pdf | Exercise VI]]
|-
| ''Practical''
| align="right" |''' 27.02.2017'''
| [[Media:lecture5.pdf | Tutorial I: Mininet: Basics, OpenFlow and Network Topologies]]
| [[Media:ex7_2017.pdf | Exercise VII]]
| Lecture V: OpenStack
| [[Media:OpenStack_Tutorial.pdf | Tutorial IV: OpenStack]]
|-
|'' Theory/Practical''
| align="right" | '''28.02.2017'''
| Lecture VI: Enhancing Data Plane - I
| Tutorial V: Geant Test Bed
| Lecture VII: Enhancing Data Plane - II
| Exercise VIII
|-
|'' Theory/Practical''
| align="right" | '''01.03.2017'''
| Lecture VIII: NFV - I
| Tutorial VI: NetVM
| Lecture IX: NFV - II
| Exercise IX
|-
|'' Theory/Practical''
| align="right" | '''02.03.2017'''
| Lecture X: Load Balancing
| Tutorial VII: ONOS
| Lecture XI: SDN for related fields
| Exercise XII
|-
| ''Seminar''
| align="right" | '''06/07.04.2017'''
| Final Presentations
| Final Presentations
| Final Presentations
| Final Presentations
|-
| ''Seminar''
| align="right" | '''30.04.2017'''
| Final Submissions due
|
|
|
|-
|}

'''** Note: this session starts at 15:15. The subsequent exercise starts at approximately 16:00.'''

[[Category:Courses]]

File:OpenStack Tutorial.pdf

2017-02-27T10:35:27Z

Debenedetto: