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The Journal of Supercomputing

Erschienen in:

01.01.2015

Distributed optimization Grid resource discovery

verfasst von: Mohammad Hasanzadeh, Mohammad Reza Meybodi

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2015

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Abstract

Grid computing is a framework for large-scale resource sharing and indexing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimization grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the recommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate.

Vorheriger Artikel PS-Cache: an energy-efficient cache design for chip multiprocessors

Nächster Artikel A fast placement algorithm for embedded just-in-time reconfigurable extensible processing platform

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Titel: Distributed optimization Grid resource discovery
verfasst von: Mohammad Hasanzadeh
Mohammad Reza Meybodi
Publikationsdatum: 01.01.2015
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 1/2015
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1289-4

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