Mining for Misconfigured Machines in Grid Systems

Noam Palatin, Arie Leizarowitz, Assaf Schuster, Ran Wolff

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter describes the Grid Monitoring System (GMS) - a system which adopts a distributed data mining approach to detection of misconfigured grid machines. The GMS non-intrusively collects data from sources available throughout the grid system. It converts raw data to semantically meaningful data and stores these data on the machine from which, it was obtained limiting incurred overhead and allowing scalability. When analysis is requested, a distributed outliers detection algorithm is employed to identify misconfigured machines. The algorithm itself is implemented as a recursive workflow of grid jobs and is especially suited to grid systems in which the machines might be unavailable most of the time or often fail altogether.

Original languageEnglish
Title of host publicationData Mining Techniques in Grid Computing Environments
PublisherJohn Wiley & Sons, Ltd
Number of pages19
ISBN (Print)9780470512586
StatePublished - 22 Jun 2009
Externally publishedYes


  • Data analysis
  • Distributed HilOut algorithm
  • GMS ontology - a job
  • GMS using Condor DAGman
  • Grid Monitoring System (GMS)
  • Grid system misconfigured machine mining
  • Knowledge discovery process - acquiring
  • Large grid system misconfigured machine detection
  • Pre-processing and storing data
  • System data acquisition - intrusive and non-intrusive
  • a pool and matchmaking

ASJC Scopus subject areas

  • General Computer Science


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