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Cloud benchmarking for maximising performance of scientific applications

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Varghese_2016_Cloud_IEEETCC_AAM.pdf (4.763Mb)
Date
01/01/2019
Author
Varghese, Blesson
Akgun, Ozgur
Miguel, Ian James
Thai, Long Thanh
Barker, Adam David
Keywords
Cloud benchmarking
Cloud performance
Benchmarking methodology
Cloud ranking
QA75 Electronic computers. Computer science
Computer Networks and Communications
Software
NDAS
BDC
R2C
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Abstract
How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings - one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.
Citation
Varghese , B , Akgun , O , Miguel , I J , Thai , L T & Barker , A D 2019 , ' Cloud benchmarking for maximising performance of scientific applications ' , IEEE Transactions on Cloud Computing , vol. 7 , no. 1 , 7553491 , pp. 170-182 . https://doi.org/10.1109/TCC.2016.2603476
Publication
IEEE Transactions on Cloud Computing
Status
Peer reviewed
DOI
https://doi.org/10.1109/TCC.2016.2603476
ISSN
2168-7161
Type
Journal article
Rights
© 2016, IEEE. 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 https://doi.org/10.1109/TCC.2016.2603476
Description
This research was pursued under the EPSRC grant, EP/K015745/1, a Royal Society Industry Fellowship and an AWS Education Research grant.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/9538

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