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DatArcs Is Aiming For Dynamically-Tuned, Self-Optimizing Linux Servers
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Originally posted by cj.wijtmans View PostSo basically its doing what a scheduler should be doing just in a wider area? Could it not be contributed as module into the kernel?
You run an experiment with settings S[t]. You observe the outcome O[t]. Based on the outcome you tweak the settings a bit and start another time period with settings S[t+1]. Repeat. Eventually you can converge on a solution that's near-optimal according to the chosen criteria (e.g. throughput, latency, energy use, cost, ...)
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Guest repliedUbuntu 16.40
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Relatedly, Google's DeepMind project was used to cut cooling costs via machine learning. This makes me hopeful that one might be able to use ML to optimize energy use and performance across the board, at many layers of the stack.
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Ah cool, we needed some "your pc is slow, install $random_bs" on Linux too. /sarcasm
More seriously, this is very interesting even if closed source, as the config changes are still readable and being automated it can do much more accurate testing than a human can.
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Originally posted by cj.wijtmans View PostSo basically its doing what a scheduler should be doing just in a wider area? Could it not be contributed as module into the kernel?
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So basically its doing what a scheduler should be doing just in a wider area? Could it not be contributed as module into the kernel?
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DatArcs Is Aiming For Dynamically-Tuned, Self-Optimizing Linux Servers
Phoronix: DatArcs Is Aiming For Dynamically-Tuned, Self-Optimizing Linux Servers
DatArcs is a new software start-up aiming to provide software to dynamically tune Linux servers for maximum performance and energy efficiency in the data-center. The DatArcs optimizer analyzes the server's workload over time and optimizes the server "several times per minute" to achieve better performance or lower power use...
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