Algorithms for optimising heterogeneous Cloud virtual machine clusters
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It is challenging to execute an application in a heterogeneous cloud cluster, which consists of multiple types of virtual machines with different performance capabilities and prices. This paper aims to mitigate this challenge by proposing a scheduling mechanism to optimise the execution of Bag-of-Task jobs on a heterogeneous cloud cluster. The proposed scheduler considers two approaches to select suitable cloud resources for executing a user application while satisfying pre-defined Service Level Objectives (SLOs) both in terms of execution deadline and minimising monetary cost. Additionally, a mechanism for dynamic re-assignment of jobs during execution is presented to resolve potential violation of SLOs. Experimental studies are performed both in simulation and on a public cloud using real-world applications. The results highlight that our scheduling approaches result in cost saving of up to 31% in comparison to naive approaches that only employ a single type of virtual machine in a homogeneous cluster. Dynamic reassignment completely prevents deadline violation in the best-case and reduces deadline violations by 95% in the worst-case scenario.
Thai , L T , Varghese , B & Barker , A D 2016 , Algorithms for optimising heterogeneous Cloud virtual machine clusters . in 2016 IEEE International Conference on Cloud Computing Technology and Science . , 7830674 , IEEE , pp. 118-125 , 8th IEEE International Conference on Cloud Computing Technology and Science , Luxembourg , 12-15 December . DOI: 10.1109/CloudCom.2016.0033conference
2016 IEEE International Conference on Cloud Computing Technology and Science
© 2016, IEEE. This work has been 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 ieeexplore.ieee.org / 10.1109/CloudCom.2016.0033
DescriptionThis research was supported by an Amazon Web Services Education Research grant.
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