Multi-scale real-time grid monitoring with job stream mining

Xiangliang Zhang*, Michèle Sebag, Germain Renaud Cécile

*Corresponding author for this work

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

4 Scopus citations

Abstract

The ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The GSTRAP system, embedding the STRAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administrator with a consolidated view of the workload, enabling the visual inspection of its long-term trends.

Original languageEnglish (US)
Title of host publication2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009
Pages420-427
Number of pages8
DOIs
StatePublished - Oct 13 2009
Event2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009 - Shanghai, China
Duration: May 18 2009May 21 2009

Publication series

Name2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009

Other

Other2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009
CountryChina
CityShanghai
Period05/18/0905/21/09

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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