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Statistical techniques for online anomaly detection in data centers

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6 Author(s)
Chengwei Wang ; CERCS, Georgia Inst. of Technol., Atlanta, GA, USA ; Viswanathan, K. ; Choudur, L. ; Talwar, V.
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Online anomaly detection is an important step in data center management, requiring light-weight techniques that provide sufficient accuracy for subsequent diagnosis and management actions. This paper presents statistical techniques based on the Tukey and Relative Entropy statistics, and applies them to data collected from a production environment and to data captured from a testbed for multi-tier web applications running on server class machines. The proposed techniques are lightweight and improve over standard Gaussian assumptions in terms of performance.

Published in:

Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium on

Date of Conference:

23-27 May 2011