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Algorithms for optimising heterogeneous Cloud virtual machine clusters

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Date
12/12/2016
Author
Thai, Long Thanh
Varghese, Blesson
Barker, Adam David
Keywords
QA75 Electronic computers. Computer science
T Technology
NDAS
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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.
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
 
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Publication
2016 IEEE International Conference on Cloud Computing Technology and Science
DOI
https://doi.org/10.1109/CloudCom.2016.0033
Type
Conference item
Rights
© 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
Description
This research was supported by an Amazon Web Services Education Research grant.
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  • Computer Science Research
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/9950

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