[NetDev-People] 0x14: Moonshot talks + Workshop, Machine Learning For Networking
Jamal Hadi Salim
jhs at mojatatu.com
Sun Feb 2 18:38:16 UTC 2020
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:
- improve datapath performance
- path optimization
- anomaly detection from a baseline expectation and using the
resulting data either for security or optimization end goals
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
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.
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.
Reminder, registration is now open and early bird is still in effect.
More information about the people