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Seminar on Internet Technologies (Summer 2017)

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Details

Workload/ECTS 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: Dr. Hong Huang
Teaching assistant: Tao Zhao
Time: Apr 20, 16:00ct: Introduction Meeting
Place: IFI Building, Room 3.101
UniVZ [1]


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:[2]).
    • 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

Topic Topic Advisor 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.

Hong Huang [3]
Inferring social capital from big data

This study is to discover the state of art of social capital measuring, particularly, from big data perspective.

Hong Huang [4][5]
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.

Hong Huang [6]
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.

Osamah Barakat [7][8][9]
Bitcoin: state of the art and position paper

This study is to provide a comprehensive study of the current situation on Bitcoin. Latest advances in its structure, security and furture.

Osamah Barakat [10][11]
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.

Osamah Barakat a good start from [12][13]
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.

Enhuan Dong [14][15][16][17][18]
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.

Enhuan Dong [19][20][21]
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.

Enhuan Dong [22][23][24][25][26][27][28]
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.

Shichang Ding [29][30]
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.

Shichang Ding [31]
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.

Shichang Ding [32][33]
Mobile network data for public health - A Survey

This study is to provide a comprehensive study of Mobile network data for public health.

Tao Zhao [34]
Understanding and modelling individual human mobility

This study is to provide a comprehensive study of understanding and modelling individual human mobility.

Tao Zhao Take a look at related papers in well known conferences/workshops/journals, e.g., [35]
Recommendations in Location-based Social Networks - A Survey

This study is to provide a comprehensive study of recommendations in Location-based Social Networks.

Tao Zhao [36]
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
- NDN technical report
- ICN Base line scenarios
Sripriya Adhatarao (sripriya-srikant.adhatarao@informatik.uni-goettingen.de) For general introduction:
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.

Sameer Kulkarni [37] [38] [39] [40] [41]
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.

Sameer Kulkarni [42] [43] [44]
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.

Sameer Kulkarni [45] [46] [47] [48] [49]
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.

Sameer Kulkarni [50] [51] [52] [53]
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.

David Koll [54]
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.

David Koll [55]
How do self-driving cars work?

The topic title is pretty self-explanatory :)

David Koll [56]
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