DocLite : a Docker-based Lightweight Cloud benchmarking tool
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Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks.The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.
Varghese , B , Subba , L T , Thai , L T & Barker , A D 2016 , DocLite : a Docker-based Lightweight Cloud benchmarking tool . in 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) . , 7515691 , IEEE Computer Society , pp. 213-222 , 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid 2016) , Cartegena , Colombia , 16/05/16 . https://doi.org/10.1109/CCGrid.2016.14conference
2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)
© 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/CCGrid.2016.14
DescriptionThis research was pursued under the EPSRC grant, EP/K015745/1, a Royal Society Industry Fellowship, an Erasmus Mundus Master’s scholarship and an AWS Education Research grant.
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