Enhancing the effective utilisation of grid clusters by exploiting on-line performability analysis

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


In grid applications the heterogeneity and potential failures of the computing infrastructure poses significant challenges to efficient scheduling. Performance models have been shown to be useful in providing predictions on which schedules can be based (N. Furmento et al., 2002) and most such techniques can also take account of failures and degraded service. However, when several alternative schedules are to be compared it is vital that the analysis of the models does not become so costly as to outweigh the potential gain of choosing the best schedule. Moreover, it is vital that the modelling approach can scale to match the size and complexity of realistic applications. In this paper, we present a novel method of modelling job execution on grid compute clusters. As previously we use performance evaluation process algebra (PEPA) (J. Hillston, 1996) as the system description formalism, capturing both workload and computing fabric. The novel feature is that we make a continuous approximation of the state space underlying the PEPA model and represent it as a set of ordinary differential equations (ODEs) for solution, rather than a continuous time, but discrete state space, Markov chain.
Original languageEnglish
Title of host publicationCluster Computing and the Grid, 2005. CCGrid 2005. IEEE International Symposium on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)0-7803-9074-1
Publication statusPublished - 1 May 2005


  • differential equations
  • grid computing
  • process algebra
  • workstation clusters
  • Markov chains
  • PEPA model
  • continuous approximation
  • discrete state space
  • grid clusters
  • grid compute clusters
  • job execution modelling
  • online performability analysis
  • ordinary differential equations
  • performance evaluation process algebra
  • performance models
  • scheduling
  • system description formalism
  • Computational efficiency
  • Computational modeling
  • Differential equations
  • Engines
  • Grid computing
  • Hardware
  • Informatics
  • Performance analysis
  • Processor scheduling
  • State-space methods


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