Advanced Topics in AI for Networking (Summer 2022): Difference between revisions
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{{CourseDetails | {{CourseDetails | ||
|credits=5 ECTS | |credits=5 ECTS | ||
|module=M.Inf. | |module=M.Inf.1123 (new Regulations) | ||
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan, Dr. Tingting Yuan] | |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/dr-tingting-yuan, Dr. Tingting Yuan] | ||
|ta=[NA] | |ta=[NA] | ||
|time= | |time=Wednesday 14:00-16:00 | ||
|place=IfI 0.101 | |place=IfI 0.101 | ||
}} | }} | ||
==Announcements== | ==Announcements== | ||
Please contact me by email: | Please contact me by email:tingting.yuan@cs.uni-goettingen.de if you have any questions. | ||
==Course Overview== | ==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: | 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 | * Weekly Presentation + Weekly Paper Reading and Discussion 40% | ||
* Final Presentation | * Final Presentation 30% | ||
* Final Report | * 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. | 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. | ||
| Line 29: | Line 25: | ||
** Summary of the paper | ** Summary of the paper | ||
** Pros and cons of the paper (your conclusion) | ** Pros and cons of the paper (your conclusion) | ||
** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 | ** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 Tuesday). => the review form is available at [[http://user.informatik.uni-goettingen.de/~fu/Paper_Review_Form_ATCN_WS201112.doc 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. | * 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: | * In the middle of the semester, everyone is requested to prepare: | ||
| Line 36: | Line 32: | ||
==List of Papers == | ==List of Papers == | ||
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704] | Demo: Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning [https://www.usenix.org/conference/osdi21/presentation/qiao][A] | ||
1. Lightweight and Robust Representation of Economic Scales from Satellite Imagery [https://ojs.aaai.org/index.php/AAAI/article/download/5379/5235] [L] | |||
2. AutoML for Video Analytics with Edge Computing [https://ieeexplore.ieee.org/abstract/document/9488704][A] | |||
3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448] | 3. Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision [https://dl.acm.org/doi/10.1145/3300061.3345448][A] | ||
4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882] | 4. NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices [https://dl.acm.org/doi/10.1145/3458864.3467882] [B] | ||
5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874] | 5. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [https://dl.acm.org/doi/10.1145/3387514.3405874] [B] | ||
6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] | 6. Deep Interest Network for Click-Through Rate Prediction [https://arxiv.org/pdf/1706.06978.pdf] [L] | ||
7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070] | 7. Energy-Efficient 3D Vehicular Crowdsourcing For Disaster Response by Distributed Deep Reinforcement Learning [https://dl.acm.org/doi/pdf/10.1145/3447548.3467070] [B] | ||
8. 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] | ||
9. Routing on Multiple Optimality Criteria[https:// | 9. Routing on Multiple Optimality Criteria[https://dl.acm.org/doi/pdf/10.1145/3387514.3405864] | ||
10. 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] | ||
11. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] | 11. Understanding Lifecycle Management Complexity of Datacenter Topologies [https://www.cs.jhu.edu/~huang/paper/omegagen-nsdi20-preprint.pdf] [L] | ||
12. ACC: Automatic ECN Tuning for High-Speed Datacenter Networks [https://dl.acm.org/doi/pdf/10.1145/3452296.3472927][A] | |||
13. Seven Years in the Life of Hypergiants’ Off-Nets [https://dl.acm.org/doi/pdf/10.1145/3452296.3472928] | |||
14. ATP: In-network Aggregation for Multi-tenant Learning [https://www.usenix.org/system/files/nsdi21-lao.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] | |||
16. MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation [https://www.usenix.org/system/files/osdi21-kumar.pdf] | |||
==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 Presentations & Report== | ||
Latest revision as of 10:03, 20 December 2022
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)
- Final Report:
- Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
- Due by 23:59pm