Advanced Topics in AI for Networking (Winter 2022/2023)

From NET Wiki
Jump to navigation Jump to search

Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1123
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan
Teaching assistant: [NA]
Time: Thursday 14:00-16:00
Place: IfI 0.101


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 on Wedesday). => 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 [1] [Celine]

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

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

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

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

6. Routing on Multiple Optimality Criteria[6]

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

8. Understanding Lifecycle Management Complexity of Datacenter Topologies [8] [Celine]

9. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [9] [Tim]

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

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

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

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

14. Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution [14]

Schedule

W1: Open Talk (27.10)

W2: Assignment papers (03.11)

W4: Paper ID: (17.11) #4 #1

W6: Paper ID: (01.12 online) #9 #8

W8: Paper ID: (22.12 online) #3 #10

W10: Rehearsal: (12.01) #3 #8

!! 10.01 deadline for registration on Flexnow

Final Presentation (19.01)

  • 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)


Retrieved from "https://wiki.net.informatik.uni-goettingen.de/index.php?title=Advanced_Topics_in_AI_for_Networking_(Winter_2022/2023)&oldid=7955"