Twelve years ago being hired as a Capacity Planner first time and not knowing anything about the specialty, I was asked to look at computer measurements to do any analysis. Applying my academic researcher skills I noticed that server's capacity usage usually shows different seasonality patterns (shifts, weekends, lunch times and so on); so my first impulse was to decompose that to several signals using Fourier Transform. I formulate the task to my manager, but for some reason (to much math and computing power requirements?) he suggested to use just standard statistical approaches instead, and I did…
I did develop and implement SEDS methodology (statistical exception detection) that naturally covers daily and weekly cycles and potentially can uncover monthly and yearly ones if there is enough data history to build baseline.
But Fourier analysis was still in my mind, and I have just been waiting for the time when our computational capacity would be enough to apply the method against system performance data….
That’s why I was positively surprised when I noticed the following paper in CMG’12 agenda:
Introduction to Wavelets and their Application for Computer Performance Trend and Anomaly Detection Dima Seliverstov, BMC Software
ABSTRACT: This paper presents a technique to identify trends and anomalies in Performance data using wavelets.
And of course, I planed (see my past post here), attended and enjoyed that, I know Dima via other CMG events, spoke with him a few times and have already analyzed some other his papers in this blog (The Exception Value Concept to Measure Magnitude of Systems Behavior Anomalies).
His "wavelet" paper is about implementation of my old dream to use something like Fourier analysis and his idea of decomposing the performance data to combination of typical wavelets is a good attempt to do that. Especially impressive was to see the "Scalogram that is a type of a heat-map to show the location of the energy as a function of frequency and time”: “Scalogram is a heat map for wavelets transformation”. Some interesting examples (including against VMware VCenter data) were presented and was calculated by MATLAB tool:
BTW stock market is already adopting this idea, the paper references that: Wavelets for Stock Market Analysis
Why not us?
… Ironically the same manager asks me now to analyze how business cycles drive capacity usage….
I have got the comment on my previous post “ BIRT based Control Chart “ with questions about how actually in BIRT the data are prepared for ...
Your are welcome to post to this blog any message related to the Capacity, Performance and/or Availability of computer systems. Just put you...
Thursday, April 18, 2013
Identify trends and anomalies by using wavelets - CMG'12 paper
He started in 1979 as IBM/370 system engineer. In 1986 he got his PhD. in Robotics at St. Petersburg Technical University (Russia) and then worked as a professor teaching CAD/CAM, Robotics for 12 years. He published 30+ papers and made several presentations for conferences related to the Robotics and Artificial Intelligent fields. In 1999 he moved to the US, worked at Capital One bank as a Capacity Planner. His first CMG.org paper was written and presented in 2001. The next one, "Exception Detection System Based on MASF Technique," won a Best Paper award at CMG'02 and was presented at UKCMG'03 in Oxford, England. He made other tech. presentations at IBM z/Series Expo, SPEC.org, Southern and Central Europe CMG and ran several workshops covering his original method of Anomaly and Change Point Detection (Perfomalist.com). Author of “Performance Anomaly Detection” class (at CMG.com). Worked 2 years as the Capacity team lead for IBM, worked for SunTrust Bank for 3 years and then at IBM for 3 years as Sr. IT Architect. Now he works for Capital One bank as IT Manager at the Cloud Engineering and since 2015 he is a member of CMG.org Board of Directors. Runs UT channel iTrubin