in reply to Re^3: Time series normalization
in thread Time series normalization
Each managed system could easily contain 20 logical partitions, and for a 3 month trend it could be about 3500 - 5000 values per LPAR.
Using the "select just the 100% common times" method takes about 30 odd seconds to do such a graph for nearly 20 members of the managed system, but to get this result took quite a bit of SQLite3 tuning.
The really big problem with just inserting undefs would be the number of samples. If even just one server was set to gather stats at a short interval, each of the other servers involved running at a different interval would now have to include extra empty values.
I would therefore be inclined to discard the times for which less than x % of hosts have values, but is this the best solution? Since I have the number of samples per data-series, is there no way to fit each data-series between a start and end time using some sort of approximation or mathematical transform?
Niel
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Re^5: Time series normalization
by jrsimmon (Hermit) on Jul 16, 2009 at 19:00 UTC |