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dc.contributor.authorBarrett, Enda
dc.contributor.authorHowley, Enda
dc.contributor.authorDuggan, Jim
dc.identifier.citationBarrett, E., Howley, E., & Duggan, J. (14-16 Sept. 2011). A learning architecture for scheduling workflow applications in the cloud. Paper presented at the Web Services (ECOWS), 2011 Ninth IEEE European Conference on.en_US
dc.descriptionConference paperen_US
dc.description.abstractThe scheduling of workflow applications involves the mapping of individual workflow tasks to computational resources, based on a range of functional and non-functional quality of service requirements. Workflow applications such as scientific workflows often require extensive computational processing and generate significant amounts of experimental data. The emergence of cloud computing has introduced a utility-type market model, where computational resources of varying capacities can be procured on demand, in a pay-per-use fashion. In workflow based applications dependencies exist amongst tasks which requires the generation of schedules in accordance with defined precedence constraints. These constraints pose a difficult planning problem, where tasks must be scheduled for execution only once all their parent tasks have completed. In general the two most important objectives of workflow schedulers are the minimisation of both cost and make span. The cost of workflow execution consists of both computational costs incurred from processing individual tasks, and data transmission costs. With scientific workflows potentially large amounts of data must be transferred between compute and storage sites. This paper proposes a novel cloud workflow scheduling approach which employs a Markov Decision Process to optimally guide the workflow execution process depending on environmental state. In addition the system employs a genetic algorithm to evolve workflow schedules. The overall architecture is presented, and initial results indicate the potential of this approach for developing viable workflow schedules on the Cloud.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.relation.ispartofThe European Conference on Web Servicesen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectMarkov processesen_US
dc.subjectCloud computingen_US
dc.subjectGenetic algorithmsen_US
dc.subjectQuality of serviceen_US
dc.subjectSoftware architectureen_US
dc.subjectStorage managementen_US
dc.subjectWorkflow management softwareen_US
dc.subjectMarkov decision processen_US
dc.subjectCloud workflow schedulingen_US
dc.subjectComputational costsen_US
dc.subjectComputational processingen_US
dc.subjectComputational resourcesen_US
dc.subjectData transmission costsen_US
dc.subjectEnvironmental stateen_US
dc.subjectGenetic algorithmen_US
dc.subjectLearning architectureen_US
dc.subjectPay-per-use fashionen_US
dc.subjectPrecedence constraintsen_US
dc.subjectQuality of service requirementsen_US
dc.subjectScheduling workflow applicationsen_US
dc.subjectScientific workflowsen_US
dc.subjectStorage sitesen_US
dc.subjectUtility-type market modelen_US
dc.subjectWorkflow based applications dependencyen_US
dc.subjectWorkflow execution processen_US
dc.subjectWorkflow schedulersen_US
dc.subjectWorkflow tasksen_US
dc.subjectBiological cellsen_US
dc.subjectOptimal schedulingen_US
dc.subjectProcessor schedulingen_US
dc.subjectBayesian model learningen_US
dc.subjectWorkflow schedulingen_US
dc.titleA learning architecture for scheduling workflow applications in the clouden_US
dc.typeConference Paperen_US
dc.local.contactJames Duggan, Dept. Of Information Technology, I.T. Building, Nui Galway. 3336 Email:

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland