Minimising the execution of unknown Bag-of-Task jobs with deadlines on the Cloud
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Scheduling 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.
Thai , 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 , 1-1 June . DOI: 10.1145/2912152.2912153workshop
DIDC '16 Proceedings of the ACM International Workshop on Data-Intensive Distributed Computing
© 2016, the Author(s). 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 dl.acm.org / https://dx.doi.org/10.1145/1235
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