TY - GEN
T1 - Statistical techniques for online anomaly detection in data centers
AU - Wang, Chengwei
AU - Viswanathan, Krishnamurthy
AU - Choudur, Lakshminarayan
AU - Talwar, Vanish
AU - Satterfield, Wade
AU - Schwan, Karsten
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80052780610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052780610&partnerID=8YFLogxK
U2 - 10.1109/INM.2011.5990537
DO - 10.1109/INM.2011.5990537
M3 - Conference contribution
AN - SCOPUS:80052780610
SN - 9781424492213
T3 - Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011
SP - 385
EP - 392
BT - Proceedings of the 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011
T2 - 12th IFIP/IEEE International Symposium on Integrated Network Management, IM 2011
Y2 - 23 May 2011 through 27 May 2011
ER -