Plug and Play Bench : simplifying big data benchmarking using containers
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The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application. In this paper, we present Plug And Play Bench (PAPB1): aninfrastructure aware abstraction built to integrate and simplifythe deployment of big data benchmarking tools on clusters of machines. PAPB automates the tedious process of installing, configuring and executing common big data benchmark work-loads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The paper further illustrates the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform.
Ceesay , S , Barker , A D & Varghese , B 2017 , Plug and Play Bench : simplifying big data benchmarking using containers . in J-Y Nie , Z Obradovic , T Suzumura , R Ghosh , R Nambiar , C Wang , H Zang , R Baeza-Yates , X Hu , J Kepner , A Cuzzocrea , J Tang & M Toyoda (eds) , Proceedings 2017 IEEE International Conference on Big Data (IEEE BigData 2017) . , 8258249 , IEEE Computer Society , pp. 2821-2828 , Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD) , Boston , United States , 11/12/17 . https://doi.org/10.1109/BigData.2017.8258249workshop
Proceedings 2017 IEEE International Conference on Big Data (IEEE BigData 2017)
© IEEE, 2017. 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 https://doi.org/10.1109/BigData.2017.8258249
DescriptionThis research was supported by a Microsoft Azure Award.
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