Pro .NET Benchmarking by Andrey Akinshin

Author:Andrey Akinshin
Language: eng
Format: epub
ISBN: 9781484249413
Publisher: Apress

Foo

543 ms

104 ms

-439 ms

Bar

108 ms

560 ms

452 ms

Baz

94 ms

101 ms

7 ms

Qux

103 ms

105 ms

2 ms

Quux

102 ms

99 ms

-3 ms

Quuz

98 ms

96 ms

-2 ms

Correlated changes in time series

If you can detect a correlation between two time series in your tests, it can be interesting to check that you always have this correlation. In Table 5-7, you can see an example of some latency and throughput measurements. The latency is just a raw duration, the throughput is a number of RPS. We run these tests on different agents with different hardware, so we can’t apply “usual” degradation analysis here. However, we can notice a pattern: Throughput≈2 sec / Latency. For example, if Latency = 0.1 sec, we get Throughput = 2 sec / 0.1 sec = 20. This pattern can be explained by parallelization: we have two threads on each agent that process our requests. We can observe such patterns on all agents except Agent4. So, we can assume that something is wrong with parallelization here. Of course, we can detect this problem in other ways. However, the correlation analysis helped us to formulate a hypothesis for future investigation (something is wrong with the Latency/Throughput) and get additional important information (we have this problem only on Agent4). Such facts can save a lot of investigator time because you can collect all such suspicious patterns automatically. You can find another example of such analysis in [AnomalyIo 2017].Table 5-7.An Example of Correlated Changes in Time Series

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.