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dc.contributor.authorThai, Long Thanh
dc.contributor.authorVarghese, Blesson
dc.contributor.authorBarker, Adam David
dc.date.accessioned2016-05-31T23:33:13Z
dc.date.available2016-05-31T23:33:13Z
dc.date.issued2016-06-01
dc.identifier241609042
dc.identifier49357750-3ad6-4ad9-87d3-70dde23c8a59
dc.identifier84978823996
dc.identifier000390302200002
dc.identifier.citationThai , L T , Varghese , B & Barker , A D 2016 , Minimising the execution of unknown Bag-of-Task jobs with deadlines on the Cloud . in DIDC '16 Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing . ACM , pp. 3-10 , The 7th International Workshop on Data-intensive Distributed Computing (DIDC'16) , Kyoto , Japan , 1/06/16 . https://doi.org/10.1145/2912152.2912153en
dc.identifier.citationworkshopen
dc.identifier.isbn9781450343527
dc.identifier.urihttps://hdl.handle.net/10023/8907
dc.description.abstractScheduling jobs with deadlines, each of which de nes the latest time that a job must be completed, can be challenging on the cloud due to incurred costs and unpredictable performance. This problem is further complicated when there is not enough information to e ectively schedule a job such that its deadline is satis ed, and the cost is minimised. In this paper, we present an approach to schedule jobs, whose performance are unknown before execution, with deadlines on the cloud. By performing a sampling phase to collect the necessary information about those jobs, our approach delivers the scheduling decision within 10% cost and 16% violation rate when compared to the ideal setting, which has complete knowledge about each of the jobs from the beginning. It is noted that our proposed algorithm outperforms existing approaches, which use a xed amount of resources by reducing the violation cost by at least two times.
dc.format.extent1192887
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofDIDC '16 Proceedings of the ACM International Workshop on Data-Intensive Distributed Computingen
dc.subjectBag of Tasken
dc.subjectSchedulingen
dc.subjectDeadlineen
dc.subjectCloud computingen
dc.subjectUnknownen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subject.lccQA75en
dc.titleMinimising the execution of unknown Bag-of-Task jobs with deadlines on the Clouden
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1145/2912152.2912153
dc.date.embargoedUntil2016-06-01


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