In recent years, we have witnessed the widespread usage of ML tools for various classification, detection, and control problems. More recently, we have witnessed the use of ML for various networking problems as well. However, operationalizing ML solutions for networked systems is more nuanced than simply calibrating existing tools, developed for other domains (image classification, NLP, etc.). More in-depth exploration to develop flexible, scalable, and generalizable ML-based networked systems. In this course, we will cover recent research, published at top networked systems (USENIX NSDI, ACM SIGCOMM) and ML conferences (NeurIPS, ICML, etc.), that developed new ML tools/techniques for networked systems. In the process, we will learn how to identify problems that can (or cannot) benefit from ML, decide which tool/algorithm to use, and how to do interdisciplinary research covering networking, ML, and systems.
There are no official prerequisites; however, a basic familiarity with networking, ML, and distributed systems concepts will be very helpful.
Location: Phelps 3526
Time: 5-6:50 PM, Tuesday and Thursday
Instructor: Arpit Gupta (firstname.lastname@example.org).
My office is 1117 Harold Frank Hall (HFH).
Course website: https://netai-ucsb.github.io. All course materials will be posted on the course website, and students will be expected to provide materials to add to this site.
Slack: https://netai-ucsb.slack.com. We will use a slack group for class communications. You can join using any @ucsb.edu email address. You can also create slack channels for your team communications.
This schedule is tentative. Please refer to this page to get the most recent version of the schedule.
|Jan 7th||Intro + logistics|
|Jan 9th||Self-driving networks||Self-driving networks
Measurements for self-driving networks
An Effort to Democratize Networking Research in the Era of AI/ML
|Jan 14||Software-defined networking (SDN)||Road to SDN
|Jan 16||Data collection||Required:
|Jan 21||Data collection + analysis||Sonata|
|Jan 23||AI/ML for networks (video streaming)||Beauty and the Burst: Remote Identification of Encrypted Video Streams|
|Jan 28||AI/ML for networks (video streaming)||Required:
|Jan 30||AI/ML for networks (video streaming)||Neural Adaptive Video Streaming with Pensieve|
|Feb 4||AI/ML for networks (video streaming)||Contd. Neural Adaptive Video Streaming with Pensieve
Additional Reading: A View On Deep Reinforcement Learning in System Optimization
|Feb 6||Project proposals|
|Feb 11||AI/ML for networks (network management)||CableMon: Improving the Reliability of Cable Broadband Networks via Proactive Network Maintenance|
|Feb 13||AI/ML for networks (network management)||Cellular Network Traffic Scheduling with Deep Reinforcement Learning|
|Feb 18||AI/ML for networks (routers)||Neural packet classification
Additional reading: Learning to Route
|Feb 20||AI/ML for networks (hosts)||A deep reinforcement learning perspective on internet congestion control|
|Feb 25||AI/ML for networks (revisit video streaming)||Learning in situ: a randomized experiment in video streaming|
|Feb 27||Project Status Updates||No class (NSDI 2020)|
|Mar 3||AI/ML for distributed systems||Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning|
|Mar 5||AI/ML for distributed systems||CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics|
|Mar 10||AI/ML for distributed systems||Harvesting Randomness to Optimize Distributed Systems
Answering what-if deployment and configuration questions with wise
|Mar 12||Final project presentations|