Sorry to dissapoint folks, but we are _not_ going to have any talks on blockchain in 0x14! If this statement infuriates you then please demonstrate your outrage by submiting a proposal when the CFS for 0x15 opens up!
Now that we got that out the way...
What is Machine Learning? Machine learning (ML) is a subset of artificial intelligence(AI) that lets computer systems solve a specific task without using explicit instructions, relying on patterns and inference instead of human intervention.
But How Does ML Apply To Networking? Machine learning can be used to observe patterns in network traffic or configuration and use the resulting data for a variety of things, some sample space:
- dynamic congestion control (goodbye named congestion control algos!), see for example applicability of:
https://netdevconf.info/0x12/session.html?restructuring-endpoint-congestion-...
- improve datapath performance
- path optimization
- anomaly detection from a baseline expectation and using the resulting data either for security or optimization end goals
- etc.
At 0x14 we have two moonshot talks that look at using ML for networking on Linux. These talks will be part of the ML workshop which is debutting in 0x14. We hope to able to solicit discussions and feedback on the subject and hopefully have this workshop as a fixture in future netdev confs.
In the first moonshot talk Marta Plantykow, Piotr Raczynski, Maciej Machnikowski and Pawel Szymanski will discuss an approach to optimize networking performance alongside CPU utilization with ML. Marta et al propose an approach which will use ML to study RSS patterns and the CPU spread and then react dynamically to modify RSS hash parameters to improve CPU spread.
The authors will go over the challenges they overcame, show some performance numbers and solicit feedback.
More info: https://netdevconf.info/0x14/session.html?talk-performance-optimization-usin...
Our second talk is from Maciej Paczkowski, Aleksandra Jereczek, and Patrycja Kochmanska. In this talk Maciej et al integrate into FRR to understand how to best optimize the path selection in an environemnt with multiple simultenous link faults and incestant link flapps.
Could routing decisions better helped with ML hooks in the kernel/datapath? Could we make use of offloading some of the algos to AI hardware?
The authors will go over the challenges they overcame, and solicit feedback.
More info: https://netdevconf.info/0x14/session.html?talk-machine-learning-in-packet-ro...
Reminder, registration is now open and early bird is still in effect. https://netdevconf.info/0x14/registration.html
cheers, jamal