Advanced Topics in AI for Networking (Winter 2022/2023): Difference between revisions

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3. NeuroScaler: neural video enhancement at scale [https://dl.acm.org/doi/10.1145/3544216.3544218]
3. NeuroScaler: neural video enhancement at scale [https://dl.acm.org/doi/10.1145/3544216.3544218]


6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [L]
4. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [L]


5. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434]


8. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [https://ieeexplore.ieee.org/document/9312434]
6. Routing on Multiple Optimality Criteria[https://dl.acm.org/doi/pdf/10.1145/3387514.3405864]


9. Routing on Multiple Optimality Criteria[https://dl.acm.org/doi/pdf/10.1145/3387514.3405864]
7. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf]


10. Understanding, Detecting and Localizing Partial Failures in Large System Software [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf]
8. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] [L]


11. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] [L]
8. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927][A]


12. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927][A]
10. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928]


13. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928]
11. ATP: In-network Aggregation for Multi-tenant Learning [https://www.usenix.org/system/files/nsdi21-lao.pdf]


14. ATP: In-network Aggregation for Multi-tenant Learning [https://www.usenix.org/system/files/nsdi21-lao.pdf]
12. Segcache: a memory-efficient and scalable in-memory key-value cache for small objects[https://www.usenix.org/system/files/nsdi21-yang.pdf]


15. Segcache: a memory-efficient and scalable in-memory key-value cache for small objects[https://www.usenix.org/system/files/nsdi21-yang.pdf]
13. MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation [https://www.usenix.org/system/files/osdi21-kumar.pdf]
 
16. MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation [https://www.usenix.org/system/files/osdi21-kumar.pdf]


==Schedule==
==Schedule==

Revision as of 12:35, 23 September 2022

Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1222.Mp: Specialization Computer Networks Module Description -or- 3.10: Advanced Topics in Internet Research (II)(ITIS); M.Inf.1223 (new Regulations)
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan
Teaching assistant: [NA]
Time: Wednesday 14:00-16:00
Place: IfI 0.101
UniVZ [1]


Announcements

Please contact me by email:tingting.yuan@cs.uni-goettingen.de if you have any questions.

Course Overview

The purpose of this seminar is to discuss some advanced topics in computer networks. This course is a theory-oriented research seminar (5 ECTS, 2 SWS), held on a weekly base and comprises the following components:

  • Weekly Presentation + Weekly Paper Reading and Discussion 40%
  • Final Presentation 30%
  • Final Report 30%

The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.

Requirements

  • Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
    • Summary of the paper
    • Pros and cons of the paper (your conclusion)
    • NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 Tuesday). => the review form is available at [Paper_Review_Form_ATCN_WS201112.doc]
  • During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
  • In the middle of the semester, everyone is requested to prepare:
    • Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
    • Final presentation: each presentation lasts for ~20 minutes, plus ~10 minutes Q&A

List of Papers

1. Genet: automatic curriculum generation for learning adaptation in networking [2]

2. Multi-resource interleaving for deep learning training [3]

3. NeuroScaler: neural video enhancement at scale [4]

4. Deep Interest Network for Click-Through Rate Prediction [5] [L]

5. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [6]

6. Routing on Multiple Optimality Criteria[7]

7. Understanding, Detecting and Localizing Partial Failures in Large System Software [8]

8. Understanding Lifecycle Management Complexity of Datacenter Topologies [9] [L]

8. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [10][A]

10. Seven Years in the Life of Hypergiants’ Off-Nets [11]

11. ATP: In-network Aggregation for Multi-tenant Learning [12]

12. Segcache: a memory-efficient and scalable in-memory key-value cache for small objects[13]

13. MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation [14]

Schedule

W1: Open Talk

W2: Assignment Topics and demo paper reading

W3: Paper ID: (date)

W5: Paper ID: (date)

W7: Paper ID: (date)

!! 25.06 deadline for registration on Flexnow

W9: Rehearsal: Paper ID: (date)

Final Presentation

  • Paper Title:
  • Paper Title:

Report deadline

Final Presentations & Report

  • Final Registration in FlexNow: To Be Announced (TBA).


  • Final Presentation:
    • Each for ~20 minutes, plus ~10 minutes Q&A


  • Final Presentation Slots:
    • To Be Announced (TBA)


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