An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance

Kathiravan Srinivasan, Chuan-Yu Chang, Chao-Hsi Huang, Min-Hao Chang, Anant Sharma and Avinash Ankur
Volume: 14, No: 4, Page: 989 ~ 1009, Year: 2018
10.3745/JIPS.01.0031
Keywords: Clusters, Hadoop, MapReduce, Mobile Raspberry Pi, Single-board Computer
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Abstract
Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

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Cite this article
IEEE Style
Kathiravan Srinivasan, Chuan-Yu Chang, Chao-Hsi Huang, Min-Hao Chang, Anant Sharma, and Avinash Ankur, "An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance," Journal of Information Processing Systems, vol. 14, no. 4, pp. 989~1009, 2018. DOI: 10.3745/JIPS.01.0031.

ACM Style
Kathiravan Srinivasan, Chuan-Yu Chang, Chao-Hsi Huang, Min-Hao Chang, Anant Sharma, and Avinash Ankur, "An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance," Journal of Information Processing Systems, 14, 4, (2018), 989~1009. DOI: 10.3745/JIPS.01.0031.