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Algorithms for optimising heterogeneous Cloud virtual machine clusters
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dc.contributor.author | Thai, Long Thanh | |
dc.contributor.author | Varghese, Blesson | |
dc.contributor.author | Barker, Adam David | |
dc.date.accessioned | 2016-12-08T12:30:30Z | |
dc.date.available | 2016-12-08T12:30:30Z | |
dc.date.issued | 2016-12-12 | |
dc.identifier | 248135364 | |
dc.identifier | 97d43d17-da1d-445c-a242-446f3ad32d6b | |
dc.identifier | 85012965363 | |
dc.identifier | 000398536300017 | |
dc.identifier.citation | 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 , 12/12/16 . https://doi.org/10.1109/CloudCom.2016.0033 | en |
dc.identifier.citation | conference | en |
dc.identifier.isbn | 9781509014460 | |
dc.identifier.isbn | 9781509014453 | |
dc.identifier.uri | https://hdl.handle.net/10023/9950 | |
dc.description | This research was supported by an Amazon Web Services Education Research grant. | en |
dc.description.abstract | 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. | |
dc.format.extent | 8 | |
dc.format.extent | 1383323 | |
dc.language.iso | eng | |
dc.publisher | IEEE | |
dc.relation.ispartof | 2016 IEEE International Conference on Cloud Computing Technology and Science | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | T Technology | en |
dc.subject | NDAS | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | T | en |
dc.title | Algorithms for optimising heterogeneous Cloud virtual machine clusters | en |
dc.type | Conference item | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.doi | 10.1109/CloudCom.2016.0033 |
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