Toward autonomic grids: Analyzing the job flow with affinity streaming

Xiangliang Zhang*, Cyril Furtlehner, Julien Perez, Cecile Germain-Renaud, Michèle Sebag

*Corresponding author for this work

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

19 Scopus citations

Abstract

The Afinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007) provides an understandable, nearly optimal summary of a dataset, albeit with quadratic compu- tational complexity. This paper, motivated by Autonomic Computing, extends AP to the data streaming framework. Firstly a hierarchical strategy is used to reduce the complex- ity to O(N 1+ε); 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 ow and enabling the system administrator to spot online some sources of fail- ures.

Original languageEnglish (US)
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages987-995
Number of pages9
DOIs
StatePublished - Nov 16 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period06/28/0907/1/09

Keywords

  • Afinity propagation
  • Autonomic computing
  • Online clus- tering

ASJC Scopus subject areas

  • Software
  • Information Systems

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