Advanced Topics in AI for Networking (Summer 2022)

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Details

Workload/ECTS Credits: 5 ECTS
Module: M.Inf.1123 (new Regulations)
Lecturer: Prof. Xiaoming Fu; Dr. Tingting Yuan
Teaching assistant: [NA]
Time: Wednesday 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 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

Demo: Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning [1][A]

1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [2] [L]

2. AutoML for Video Analytics with Edge Computing [3][A]

3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [4][A]

4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [5] [B]

5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [6] [B]

6. Deep Interest Network for Click-Through Rate Prediction [7] [L]

7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [8] [B]

8. Reducing the Service Function Chain Backup Cost over the Edge and Cloud by a Self-Adapting Scheme [9]

9. Routing on Multiple Optimality Criteria[10]

10. Understanding, Detecting and Localizing Partial Failures in Large System Software [11]

11. Understanding Lifecycle Management Complexity of Datacenter Topologies [12] [L]

12. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [13][A]

13. Seven Years in the Life of Hypergiants’ Off-Nets [14]

14. ATP: In-network Aggregation for Multi-tenant Learning [15]

15. Segcache: a memory-efficient and scalable in-memory key-value cache for small objects[16]

16. MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation [17]

Schedule

W1: Open Talk

W2: Assignment Topics and demo paper reading

W3: Paper ID: 1 (04.05)

W5: Paper ID: 3, 4 (18.05)

W7: Paper ID: 5, 12 (01.06)

!! 25.06 deadline for registration on Flexnow

W9: Rehearsal: 7, demo (15.06)

Final Presentation (29.06 maybe)

  • Paper Title: 7
  • Paper Title: demo

Report deadline (30.07)

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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