Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping files

Xiangliang Zhang 1 Michèle Sebag 1 Cecile Germain-Renaud 1
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, X - École polytechnique, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : Grid systems are complex heterogeneous systems, and their modeling constitutes a highly challenging goal. This paper is interested in modeling the jobs handled by the EGEE grid, by mining the Logging and Bookkeeping files. The goal is to discover meaningful job clusters, going beyond the coarse categories of ”successfully terminated jobs” and ”other jobs”. The presented approach is a threestep process: i) Data slicing is used to alleviate the job heterogeneity and afford discriminant learning; ii) Constructive induction proceeds by learning discriminant hypotheses from each data slice; iii) Finally, double clustering is used on the representation built by constructive induction; the clusters are fully validated after the stability criteria proposed by Meila (2006). Lastly, the job clusters are submitted to the experts and some meaningful interpretations are found
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Communication dans un congrès
DSMM07, Oct 2007, Omaha, United States. 2007
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Contributeur : Cecile Germain <>
Soumis le : samedi 22 septembre 2007 - 23:08:05
Dernière modification le : jeudi 10 mai 2018 - 02:06:26
Document(s) archivé(s) le : lundi 24 septembre 2012 - 12:45:18

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Xiangliang Zhang, Michèle Sebag, Cecile Germain-Renaud. Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping files. DSMM07, Oct 2007, Omaha, United States. 2007. 〈inria-00174285〉

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