I wrote a new blog post, about monitoring, and when averages are useful: http://brooker.co.za/blog/2017/12/28/mean.html …
Yep! A lot of robust designs are very simple and low temperature at the outermost layers, and then increase in complexity and entropy towards their core. Problems often manifest as an inversion of those states.
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If we measured dynamic Kolmogorov complexity, in terms of the the number of branches actually executed in running code, I have a hunch that we'd identify "hot" and "cool" systems more easily and learn to insulate more organically.
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We'd also see big changes (deployments, new features) in behavior as a big temperature change, naturally associate it with thermal stress, and so manage it appropriately; keep the cooling on standby.
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