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
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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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Communication dans un congrès
15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Jun 2009, Paris, France. 2009
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https://hal.inria.fr/inria-00393825
Contributeur : Xiangliang Zhang <>
Soumis le : mardi 9 juin 2009 - 18:59:09
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : lundi 15 octobre 2012 - 12:11:10

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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. 2009. 〈inria-00393825〉

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