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Adaptively Detecting Changes in Autonomic Grid Computing

Xiangliang Zhang 1 Cecile Germain-Renaud 1, 2 Michèle Sebag 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and grid- running logs. Toward Autonomic Grid Computing, adaptively detecting the changes in a grid system can help to alarm the anomalies, clean the noises, and report the new patterns. In this paper, we proposed an approach of self-adaptive change detection based on the Page-Hinkley statistic test. It handles the non-stationary distribution without the assumption of data distribution and the empirical setting of parameters. We validate the approach on the EGEE streaming jobs, and report its better performance on achieving higher accuracy comparing to the other change detection methods.
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Submitted on : Saturday, November 27, 2010 - 7:47:48 PM
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Xiangliang Zhang, Cecile Germain-Renaud, Michèle Sebag. Adaptively Detecting Changes in Autonomic Grid Computing. Procs of ACS 2010, Oct 2010, Belgium. http://wiki.esi.ac.uk/ACS2010. ⟨hal-00540579⟩

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