Zobrazit minimální záznam

dc.contributor.authorIzakian, Hesam
dc.contributor.authorLadani, Behrouz Tork
dc.contributor.authorAbraham, Ajith
dc.contributor.authorSnášel, Václav
dc.date.accessioned2010-10-01T12:45:43Z
dc.date.available2010-10-01T12:45:43Z
dc.date.issued2010
dc.identifier.citationInternational journal of innovative computing, information and control. 2010, vol. 6, no. 9, p. 4219-4233.en
dc.identifier.issn1349-4198
dc.identifier.urihttp://hdl.handle.net/10084/83105
dc.description.abstractScheduling 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.isoenen
dc.publisherICIC Internationalen
dc.relation.ispartofseriesInternational journal of innovative computing, information and controlen
dc.subjectgrid computingen
dc.subjectschedulingen
dc.subjectmakespanen
dc.subjectflowtimeen
dc.subjectparticle swarm optimizationen
dc.titleA discrete particle swarm optimization approach for grid job schedulingen
dc.typearticleen
dc.identifier.locationNení ve fondu ÚKen
dc.identifier.wos000281745700028


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