A discrete particle swarm optimization approach for grid job scheduling

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dc.contributor.author Izakian, Hesam
dc.contributor.author Ladani, Behrouz Tork
dc.contributor.author Abraham, Ajith
dc.contributor.author Snášel, Václav
dc.date.accessioned 2010-10-01T12:45:43Z
dc.date.available 2010-10-01T12:45:43Z
dc.date.issued 2010
dc.identifier.citation International journal of innovative computing, information and control. 2010, vol. 6, no. 9, p. 4219-4233. en
dc.identifier.issn 1349-4198
dc.identifier.uri http://hdl.handle.net/10084/83105
dc.description.abstract Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, hetero- geneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) ap- proach for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in prob- lem search space to find optimal or near-optimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Exper- imental studies illustrate that the proposed method is more efficient and surpasses those of reported meta-heuristic algorithms for this problem. en
dc.language.iso en en
dc.publisher ICIC International en
dc.relation.ispartofseries International journal of innovative computing, information and control en
dc.subject grid computing en
dc.subject scheduling en
dc.subject makespan en
dc.subject flowtime en
dc.subject particle swarm optimization en
dc.title A discrete particle swarm optimization approach for grid job scheduling en
dc.type article en
dc.identifier.location Není ve fondu ÚK en
dc.identifier.wos 000281745700028

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