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dc.contributor.authorJaradat, Ward
dc.contributor.authorDearle, Alan
dc.contributor.authorBarker, Adam
dc.date.accessioned2016-09-20T23:34:22Z
dc.date.available2016-09-20T23:34:22Z
dc.date.issued2016-08-10
dc.identifier.citationJaradat , W , Dearle , A & Barker , A 2016 , ' Towards an autonomous decentralized orchestration system ' , Concurrency and Computation : Practice and Experience , vol. 28 , no. 11 , pp. 3164-3179 . https://doi.org/10.1002/cpe.3655en
dc.identifier.issn1532-0634
dc.identifier.otherPURE: 211254477
dc.identifier.otherPURE UUID: 67435f77-3967-4809-8cbd-d41c18a6240d
dc.identifier.otherScopus: 84945268921
dc.identifier.otherWOS: 000382651600011
dc.identifier.urihttps://hdl.handle.net/10023/9534
dc.description.abstractOrchestrating workflows needed for modern scientific data analysis presents a significant research challenge: they are typically executed in a centralized manner such that all data pass through a single compute server known as the engine, which causes unnecessary network traffic that leads to a performance bottleneck. This paper presents a scalable decentralized orchestration system that relies on a functional, high‐level data coordination language for executing workflows. This system consists of distributed execution engines, each of which is responsible for executing part of the overall workflow. It exploits parallelism in the workflow by partitioning it into smaller sub‐workflows and determines the most appropriate engines to execute them using network resource monitoring and placement analysis. This permits the computation logic of the workflow to be moved towards the services providing the data, which improves the overall execution time. The system supports data‐driven execution that allows each sub‐workflow to be executed as soon as the data needed for its execution become available from other sources. Therefore, a scheduling mechanism is not required to manage the order in which the sub‐workflows are orchestrated. This paper provides an evaluation of the proposed system, which demonstrates that decentralized orchestration provides scalability over centralized orchestration.
dc.language.isoeng
dc.relation.ispartofConcurrency and Computation : Practice and Experienceen
dc.rightsCopyright © 2015, John Wiley & Sons Ltd. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1002/cpe.3655en
dc.subjectService-oriented architectureen
dc.subjectDecentralized orchestrationen
dc.subjectData-centric workflowsen
dc.subjectPartitioningen
dc.subjectNetwork resource monitoringen
dc.subjectPlacement analysisen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccQA75en
dc.titleTowards an autonomous decentralized orchestration systemen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Office of the Principalen
dc.identifier.doihttps://doi.org/10.1002/cpe.3655
dc.description.statusPeer revieweden
dc.date.embargoedUntil2016-09-20


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