Zobrazit minimální záznam

dc.contributor.authorIzakian, Hesam
dc.contributor.authorAbraham, Ajith
dc.contributor.authorSnášel, Václav
dc.date.accessioned2009-09-18T07:16:40Z
dc.date.available2009-09-18T07:16:40Z
dc.date.issued2009
dc.identifier.citationSensors. 2009, vol. 9, issue 7, p. 5339-5350.en
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/75992
dc.description.abstractScheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO) algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem.en
dc.format.extent130973 bytescs
dc.format.mimetypeapplication/pdfcs
dc.language.isoenen
dc.publisherMolecular Diversity Preservation Internationalen
dc.relation.ispartofseriesSensorsen
dc.relation.urihttp://dx.doi.org/10.3390/s90705339en
dc.subjectdistributed heterogeneous computing systemsen
dc.subjectparticle swarm optimizationen
dc.subjectschedulingen
dc.titleMetaheuristic based scheduling meta-tasks in distributed heterogeneous computing systemsen
dc.typearticleen
dc.identifier.locationNení ve fondu ÚKen
dc.identifier.doi10.3390/s90705339
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.identifier.wos000268317000016


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Zobrazit minimální záznam