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Tuning The Kernel With A Genetic Algorithm
fsck! writes "Jake Moilanen provided a series of four patches against the 2.6.9 Linux kernel that introduce a simple genetic algorithm used for automatic tuning. The patches update the anticipatory IO scheduler and the zaphod CPU scheduler to both use the new in-kernel library, theoretically allowing them to automatically tune themselves for the best possible performance for any given workload. Jake says, 'using these patches, there are small gains (1-3%) in Unixbench & SpecJBB. I am hoping a scheduler guru will able to rework them to give higher gains.'"
Could this be extended to include other kernel parameters as well? Depending on your app, things like TCP timeouts and other muck can have a large impact. Tuning this stuff is currently somewhat of a black art. Then as the user community of the app becomes familiar after rollout, a lot of the usage patterns change. In a few cases, this means we end up having to re-tune the kernel.
If this package could be extended to the other parameters, it would save my customers a *lot* of time and money.
If nothing else, this could be a deciding factor for some of our clients to use linux instead of windows.
First thing: A GA is only truly effective if you let it exhaustively search the search space - which is why GAs are run against simulations rather than in operational systems. Imagine trying to tune a kernel at runtime by occassionally switching to random tuning parameters. I think this is extremely non-optimal. Of course, if most of the heavy lifting is done before-hand and the GA is simply examining pre-defined parameter sets on your machine, it could work. But it's not really much of a GA anymore.
As an alternative, perhaps using some form of pseduo-GA that tries to find pre-tuned parameters that most closely match your operating environment and then letting a Hill-Climbing algorithm hit it would be a better solution.
Hill climbing can also be used in a GA type manner by letting the GA determine witch parameters to climb and in what order. The climbing itself is pretty straightforward, allow vectors to interact with individual parameters. If the result is worse, reverse the vectors or switch to new parameters. Rinse, repeat.
Yes, GA can produce odd bugs and potholes. Yes, it is the fitness test that determines if that will be true. But a good GA will generally find solutions that are as good or better than hand tuning for search spaces that are very complex. Overall, this is a good idea but is probably more complex than advertised.
The reason that it can be true that 1+1 > 2 is that very peculiar nonzero value of the + operator
Would it be possible to apply a genetic algorithm on a packet scheduler? IMO the packet schedulers available today needs too much manual tweaking.