Cloud benchmarking for maximising performance of scientific applications
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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.
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
IEEE Transactions on Cloud Computing
© 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