Toward behavioral modeling of a grid system: Mining the logging and bookkeeping files

Xiangliang Zhang, Michèle Sebag, Cécile Germain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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 three-step 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. © 2007 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Number of pages6
StatePublished - Dec 1 2007
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20


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