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dc.contributor.authorLiu, Hongbo
dc.contributor.authorAbraham, Ajith
dc.contributor.authorSnášel, Václav
dc.contributor.authorMcLoone, Seán
dc.date.accessioned2012-05-11T11:34:22Z
dc.date.available2012-05-11T11:34:22Z
dc.date.issued2012
dc.identifier.citationInformation sciences. 2012, vol. 192, p. 228-243.cs
dc.identifier.issn0020-0255
dc.identifier.urihttp://hdl.handle.net/10084/90437
dc.description.abstractThe scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesInformation sciencescs
dc.relation.urihttp://dx.doi.org/10.1016/j.ins.2011.12.032cs
dc.subjectswarm intelligencecs
dc.subjectparticle swarmcs
dc.subjectscheduling problemcs
dc.subjectwork-flowcs
dc.subjectsecurity constraintscs
dc.subjectdistributed data-intensive computing environmentscs
dc.titleSwarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environmentscs
dc.typearticlecs
dc.identifier.locationNení ve fondu ÚKcs
dc.identifier.doi10.1016/j.ins.2011.12.032
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume192cs
dc.description.lastpage243cs
dc.description.firstpage228cs
dc.identifier.wos000302511900017


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