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We use a single metric, entropy, to measure a given profile’s variation. In short, entropy is a measure of the uncertainty associated with a random variable, which in this case is profile samples.
[..] When we need to identify the changes on the same entries between profiles, we calculate the Manhattan distance of two profiles...
The question is: can a similar approach be used to identify profiles that are no longer needed because the data they contain is redundant comparing to data of other profiles.
There is almost no sense to keep the profiling data for more than N days.
The exact implementation is in TBD. Open questions:
cmd/profefe
daemon or a dedicated tool for particular storage?The text was updated successfully, but these errors were encountered: