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

Xiangliang Zhang*, Michèle Sebag, Cécile Germain

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages581-586
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
CountryUnited States
CityOmaha, NE
Period10/28/0710/31/07

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint

Dive into the research topics of 'Toward behavioral modeling of a grid system: Mining the logging and bookkeeping files'. Together they form a unique fingerprint.

Cite this