Popular Post

Search This Blog

Tuesday, August 23, 2011

CMG'11 papers about non-statistical ways to capture outliers/anomalies and trends

from  The CMG'11 Abstract report  :

Monitoring Performance QoS using Outliers
Eugene Margulis, Telus
Commonly used Performance Metrics often measure technical parameters that the end user neither knows nor cares about. The statistical nature of these metrics assumes a known underlying distribution when in reality such distributions are also unknown. We propose a QoS metric that is based on counting the outliers - events when the user is clearly “dis”-satisfied based on his/her expectation at the moment. We use outliers to track long term trends and changes in performance of individual transactions as well as to track system-wide freeze events that indicate system-wide resource exhaustion.

BTW I have already tried to "count" outliers  ; see my 
2005 paper listed here: http://itrubin.blogspot.com/2007/06/system-management-by-exception.html

I used the SEDS database to count and analyze exceptions:

Introduction to Wavelets and their Application for Computer Performance Trend and Anomaly Detection: 
Introduction to wavelets and their application for computer performance analysis. Wavelets are a set of waveforms that can be used to match a signal or noise. There are various families of wavelets unlike Fourier Analysis. Wavelets are stretched(scaled) in time AND frequency and correlated with the signal. The correlation in time and frequency is displayed as a heat map. The color is the intensity, the X axis is the time and the Y axis is the frequency. The heat map shows the time the trends or anamoly starts and when it repeats(frequency).