Can AI Be Used to Fine-Tune Linux Kernel Performance?

An anonymous reader shared this report from ZDNet:

At the Linux Plumbers Conference, the invite-only meeting for the top Linux kernel developers, ByteDance Linux Kernel Engineer Cong Wang, proposed that we use AI and machine learning to tune the Linux kernel for the maximum results for specific workloads... There are thousands of parameters. Even for a Linux expert, tuning them for optimal performance is a long, hard job. And, of course, different workloads require different tunings for different sets of Linux kernel parameters... What ByteDance is working on is a first attempt to automate the entire Linux kernel parameter tuning process with minimal engineering efforts.

Specifically, ByteDance is working on tuning Linux memory management. ByteDance has found that with machine learning algorithms, such as Bayesian optimization, automated tuning could even beat most Linux kernel engineers. Why? Well, the idea, as Wang wryly put it, 'is not to put Linux kernel engineers out of business.' No, the goal is 'to liberate human engineers from tuning performance for each individual workload. While making better decisions with historical data, which humans often struggle with. And, last, but never least, find better solutions than those we come up with using our current trial and error, heuristic methods.

In short, ByteDance's system optimizes resource usage by making real-time adjustments to things like CPU frequency scaling and memory management.

Read more of this story at Slashdot.