Statistical techniques for online anomaly detection in data centers

Chengwei Wang, Krishnamurthy Viswanathan, Lakshminarayan Choudur, Vanish Talwar, Wade Satterfield, Karsten Schwan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

156 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011
Pages385-392
Number of pages8
DOIs
StatePublished - 2011
Event12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011 - Dublin, Ireland
Duration: 23 May 201127 May 2011

Publication series

NameProceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011

Conference

Conference12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011
Country/TerritoryIreland
CityDublin
Period23/05/1127/05/11

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