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The Top 5 Things I Learned While Building Anomaly Detection Algorithms for IT Ops
Toufic Boubez, Metafor Software
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
Toufic Boubez, Metafor Software
Toufic has been passionate about machine learning for over 20 years. Prior to Metafor, he was the founder and CTO of Layer 7 Technologies, a leader in API security and management and recently acquired by CA. Prior to Layer 7, Toufic was the founding CTO of Saffron Technology, a big data analytics company specializing in associative memory technology. Toufic is also a well-known SOA and Web Services pioneer and was Chief Architect for Web Services at IBM’s Software Group. He was the co-editor of the W3C WS-Policy specification, and the co-author of the OASIS WS-Trust, WS-SecureConversation, and WS-Federation submissions. He is the author of many publications, articles and several books and is one of the co-authors of the SOA Manifesto. Toufic holds a Master of Electrical Engineering degree from McGill University and a Ph.D. in Biomedical Engineering from Rutgers University.
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author = {Toufic Boubez},
title = {The Top 5 Things I Learned While Building Anomaly Detection Algorithms for {IT} Ops},
year = {2014},
address = {Seattle, WA},
publisher = {USENIX Association},
month = nov
}
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