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Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming

Xiangliang Zhang 1 Cyril Furtlehner 1 Julien Perez 2 Cécile Germain 1 Michèle Sebag 1
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 : The Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic computational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complexity to O( N^(1+e) ); the distortion loss incurred is analyzed in relation with the dimension of the data items. Secondly, a coupling with a change detection test is used to cope with non-stationary data distribution, and rebuild the model as needed. The presented approach StrAP is applied to the stream of jobs submitted to the EGEE Grid, providing an understandable description of the job flow and enabling the system administrator to spot online some sources of failures.
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Submitted on : Tuesday, June 9, 2009 - 6:59:09 PM
Last modification on : Thursday, July 8, 2021 - 3:47:45 AM
Long-term archiving on: : Monday, October 15, 2012 - 12:11:10 PM


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  • HAL Id : inria-00393825, version 1



Xiangliang Zhang, Cyril Furtlehner, Julien Perez, Cécile Germain, Michèle Sebag. Toward Autonomic Grids: Analyzing the Job Flow with Affinity Streaming. 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jun 2009, Paris, France. ⟨inria-00393825⟩



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