Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12614
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dc.contributor.authorZhu, Z-
dc.contributor.authorZhang, G-
dc.contributor.authorLi, M-
dc.contributor.authorLiu, X-
dc.date.accessioned2016-05-12T12:41:59Z-
dc.date.available2015-
dc.date.available2016-05-12T12:41:59Z-
dc.date.issued2015-
dc.identifier.citationIEEE Transactions on Parallel and Distributed Systems, 27(5): 1344 - 1357, (2015)en_US
dc.identifier.issn1045-9219-
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7127017-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12614-
dc.description.abstractCloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.en_US
dc.description.sponsorshipThis work is supported by the National Science Foundation of China under Grand no. 61272420 and the Provincial Science Foundation of Jiangsu Grand no. BK2011022.en_US
dc.format.extent1 - 1-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectCloud computingen_US
dc.subjectInfrastructure as a Serviceen_US
dc.subjectEvolutionary algorithmen_US
dc.subjectInfrastructure as a serviceen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectWorkflow schedulingen_US
dc.titleEvolutionary multi-objective workflow scheduling in Clouden_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TPDS.2015.2446459-
dc.relation.isPartOfIEEE Transactions on Parallel and Distributed Systems-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
Appears in Collections:Dept of Computer Science Research Papers

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