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AI-Powered / Machine Learning Linux Performance Tuning Is Now A Thing
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Originally posted by trivialfisYears ago, people prefixed everything with "smart" like smart phone or smart TV, which is not smart at all. Now they prefix it with "AI powered", which is just another joke.
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Originally posted by iyxwsoekthsv View PostYes, they mean something. If they actually mean something, if you know what I mean. And yes, that was intended. My point is -- In this case, there is neither a need for an actual AI-ish algorithm nor a need to actually slap that label on it. It's just an iteration through all possible configurations. If that is AI, then I've been writing AI code since I was 8.
Dynamically adjusting these to suit your current workload is a really good idea. Kind of like VTEC for your PC!
; )
Last edited by coder; 23 February 2018, 06:22 PM.
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Originally posted by Yndoendo View PostForbes magazine with "Smart TV" on the front cover, 1991, with a picture of a CRT TV.
Fast forward about 10 years, and I had a CRT HDTV that you could get with an embedded PC that could run a web browser. I just unplugged the tuner module (which I had instead of the PC box) and used it as a big external display for my laptop.
Anyone remember those LD players, with attached barcode readers? Ah, the 1980's, when all we needed was more lasers.
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Originally posted by coder View PostNo, AI is not simply a brute-force exhaustive search.
Really, server configuration is the last thing we need massive AI algorithms for. Because you can already rule out who knows how many permutations, simply because they make no sense when combined. And so forth and so on. The number of actual candidate permutations for valid configurations is really quite limited. If for no other reason then for the fact that each individual machine typically has a single workload to perform, a single task it needs to excell at. Now, if we're talking desktop use, now we're talking possible uses for more thorough analysis. Because the workload is more unpredictable.
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Originally posted by coder View Post"Really, warming our blood is the last thing we need to burn massive calories for", said one dinosaur to another.
Personally, I'd be more interested in auto-tuning the knobs themselves this technology is supposed to configure. As in, taking it one step further. Instead of merely changing the settings on the CPU scheduler in an automatic fashion, use that technology to develop better CPU schedulers. Now we're talking useful. And actually not being a dinosaur. Use the AI algorithms to evolve the tech, not to just configure the tech.
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Originally posted by iyxwsoekthsv View Postcalling me a dinosaur simply because I fail to see the use of AI algorithms here? Not going to fly.
Originally posted by iyxwsoekthsv View PostPersonally, I'd be more interested in auto-tuning the knobs themselves this technology is supposed to configure. As in, taking it one step further. Instead of merely changing the settings on the CPU scheduler in an automatic fashion, use that technology to develop better CPU schedulers. Now we're talking useful. And actually not being a dinosaur. Use the AI algorithms to evolve the tech, not to just configure the tech.
If you think this is such a simple problem, you probably think HVAC control is really trivial.
https://deepmind.com/blog/deepmind-a...oling-bill-40/Last edited by coder; 23 February 2018, 08:29 PM.
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Originally posted by coder View PostIndividual subsystems interact with each other in complex ways, and can't be optimally tuned in isolation. Worse yet, adapting them in isolation creates more potential for pathological scenarios to emerge. Or else they adapt too slowly and conservatively, failing to reap all the potential benefits.
Yes, the technology exists to come up with superior CPU schedulers. It does not have to end at merely configuring the knobs optimally. It can be extended to actually optimizing the knobs themselves. Primarily because the arguments you made do apply to actually configuring the knobs as well; subsystem interaction and so forth. So, if the challenge is so great that optimizing the knobs is impossible, configuring them should be as well. And yet, here we are... talking about a service which employs deep learning algorithms to actually do the configuration optimization. Hmmm... I'm beginning to see a pattern, a possibility...
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