On the convergence of computational and data grids

D. C. Arnold, S. S. Vahdiyar, J. J. Dongarra

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Great advances in high-performance computing have given rise to scientific applications that place large demands on software and hardware infrastructures for both computational and data services. With these trends the necessity has emerged for distributed systems developers that once distinguished between these elements to acknowledge that indeed computational and data services are tightly coupled and need to be addressed simultaneously. In this article, we compile and discuss several strategies and techniques, like co-scheduling and co-allocation of computational and data services, dynamic storage capabilities, and quality-of-service, that can be used to help resolve some of the aforementioned issues. We present our interactions with a distributed computing system, NetSolve, and a Distributed Storage Infrastructure, IBP, as a case study of how some of these techniques can be effectively deployed and offer experimental evidence from early prototypes that validate our motivation and direction.
    Original languageEnglish
    Pages (from-to)187-202
    Number of pages15
    JournalParallel Processing Letters
    Volume11
    Issue number2-3
    Publication statusPublished - Jun 2001

    Keywords

    • Computational grid
    • Data grid
    • Distributed computing
    • Heterogeneous network computing
    • Problem solving environments

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