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Advanced Topics in AI for Networking (Summer 2022): Difference between revisions
Advanced Topics in AI for Networking (Summer 2022) (view source)
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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. | ||
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** 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: | ||
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==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== | ||