Cloud Benchmarking For Maximising Performance of Scientific Applications

Blesson Varghese, Ozgur Akgun, Ian Miguel, Long Thai, Adam Barker

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
359 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)170
Number of pages1
JournalIEEE Transactions on Cloud Computing
Volume7
Issue number1
Early online date26 Aug 2016
DOIs
Publication statusEarly online date - 26 Aug 2016

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