Adaptive performance control for distributed scientific coupled models

Mohamed Hussein, Ken Mayes, Mikel Luján, John Gurd

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


    The PerCo performance control framework is capable of managing the distributed execution of scientific coupled models using migration, for example, in response to changes in an execution environment. PerCo monitors execution times and reacts according to an adaptive performance control strategy whenever serious changes of behaviour occur. A computationally cheap technique is used per model to smooth the series of monitored execution times and to provide a short-term forecast for future execution times on currently assigned resources. Where this short-term forecast fails to be achieved, the system analyses whether migration would improve matters. For models that are candidates for migration, more accurate but computationally expensive techniques are used to form a longer-term prediction of future execution times on various candidate resources. Based on the predicted gain, a migration decision is made taking account of the expected cost of migration. Experimental results for small real scientific coupled models show that the performance control strategy behaves effectively in scenarios in which the ambient load is varied during execution. Copyright 2007 ACM.
    Original languageEnglish
    Title of host publicationProceedings of the International Conference on Supercomputing|Proc Int Conf Supercomputing
    PublisherAssociation for Computing Machinery
    Number of pages9
    ISBN (Print)1595937684, 9781595937681
    Publication statusPublished - 2007
    Event21st ACM International Conference on Supercomputing, ICS07 - Seattle, WA
    Duration: 1 Jul 2007 → …


    Conference21st ACM International Conference on Supercomputing, ICS07
    CitySeattle, WA
    Period1/07/07 → …
    Internet address


    • Adaptivity
    • Coupled models
    • Grid
    • PerCo
    • Performance control


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