Decentralized Self-optimization in Shared Resource Pools

E Loureiro, Patrick Nixon, S Dobson

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.
    LanguageEnglish
    Title of host publicationStudies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications
    EditorsS Caballé, F Xhafa, A Abraham
    Place of PublicationBerlin
    Pages149-170
    Volume329
    DOIs
    Publication statusPublished - 2011

    Keywords

    • Computational resources
    • decentralized algorithms
    • decentralized optimization
    • resource containers
    • resource management
    • resource pools
    • self-optimizing
    • servers
    • utility maximization

    Cite this

    Loureiro, E., Nixon, P., & Dobson, S. (2011). Decentralized Self-optimization in Shared Resource Pools. In S. Caballé, F. Xhafa, & A. Abraham (Eds.), Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications (Vol. 329, pp. 149-170). Berlin. https://doi.org/10.1007/978-3-642-16793-5_7
    Loureiro, E ; Nixon, Patrick ; Dobson, S. / Decentralized Self-optimization in Shared Resource Pools. Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications. editor / S Caballé ; F Xhafa ; A Abraham. Vol. 329 Berlin, 2011. pp. 149-170
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    abstract = "Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.",
    keywords = "Computational resources, decentralized algorithms, decentralized optimization, resource containers, resource management, resource pools, self-optimizing, servers, utility maximization",
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    Loureiro, E, Nixon, P & Dobson, S 2011, Decentralized Self-optimization in Shared Resource Pools. in S Caballé, F Xhafa & A Abraham (eds), Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications. vol. 329, Berlin, pp. 149-170. https://doi.org/10.1007/978-3-642-16793-5_7

    Decentralized Self-optimization in Shared Resource Pools. / Loureiro, E; Nixon, Patrick; Dobson, S.

    Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications. ed. / S Caballé; F Xhafa; A Abraham. Vol. 329 Berlin, 2011. p. 149-170.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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    T1 - Decentralized Self-optimization in Shared Resource Pools

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    AU - Nixon, Patrick

    AU - Dobson, S

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    N2 - Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.

    AB - Resource pools are collections of computational resources which can be shared by different applications. The goal with that is to accommodate the workload of each application, by splitting the total amount of resources in the pool among them. In this sense, utility functions have been pointed as the main tool for enabling self-optimizing behaviour in such pools. The goal with that is to allow resources from the pool to be split among applications, in a way that the best outcome is obtained. Whereas different solutions in this context exist, it has been found that none of them tackles the problem we deal with in a total decentralized way. In this paper, we then present a decentralized and self-optimizing approach for resource management in shared resource pools.

    KW - Computational resources

    KW - decentralized algorithms

    KW - decentralized optimization

    KW - resource containers

    KW - resource management

    KW - resource pools

    KW - self-optimizing

    KW - servers

    KW - utility maximization

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    SP - 149

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    BT - Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications

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    Loureiro E, Nixon P, Dobson S. Decentralized Self-optimization in Shared Resource Pools. In Caballé S, Xhafa F, Abraham A, editors, Studies in Computational Intelligence: Intelligent Networking, Collaborative Systems and Applications. Vol. 329. Berlin. 2011. p. 149-170 https://doi.org/10.1007/978-3-642-16793-5_7