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Tuesday, June 6, 2017

Re-posting #CMGamplify - "#DataScience Tools for Infrastructure Operational Intelligence"

The following  CMG Amplify blog post written by Tim Browning is interesting as it underlines what this "System Management by Exception" blog is always about:

"...In order for the performance analyst to attend to troubled systems that may number in the thousands, it is imperative that we filter out of this vast ocean of time series metrics only those events that are anomalous and/or troubling to operational stability. It is too overwhelming to sit and look at thousands of hourly charts and tables. In addition, there is a need for continuous monitoring capability that detects problems immediately or, better yet, predicts them in the near term.  Increasingly, we need self-managing systems that learn and adapt to complex continuous activities and quickly identify the causal reconstruction of threatening conditions as well as recommend solutions (or even automatically deploy remediation events).  Out of necessity, this is where we are heading..."

and in order to  achieve that:

"..In data mining, anomaly detection (also known as outlier detection) is the search for data items in a dataset which do not conform to an expected pattern. Anomalies are also referred to as outliers, change, deviation, surprise, aberrant, peculiarity, intrusion, etc. Most performance problems are anomalies. Probably the most successful techniques (so far) would be Multivariate Adaptive Statistical Filtering (MASF) for detecting statistically extreme conditions and Relative Entropy Monitoring for detecting unusual changes in patterns of activity..."

See entire post here: https://www.cmg.org/2017/06/data-science-tools-infrastructure-operational-intelligence/

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