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Research on Single-Board Computers Clustering the Computing Performance

Author

Listed:
  • CHAO HSI HUANG

    (Department of Computer Science and Information Engineering, National Yilan University, Yilan, Taiwan)

  • MIN HAO CHANG

    (Department of Computer Science and Information Engineering, National Yilan University, Yilan, Taiwan)

  • I HSUAN LIN

    (Department of Computer Science and Information Engineering, National Yilan University, Yilan, Taiwan)

Abstract

In recent years, computers, workstations, servers, embedded systems and mobiles, scientific and technological progress showed up very rapidly, and there has been rapid popularization of Internet, network speed has upgraded very much, but with these advances large of data are produced, so in the storage or use, this form of cloud computing is a good solution. Now, many data centers providing cloud computing and cloud services solve the problem of big data in the world. We put forward setup clusters used called Hadoop on the new SBC (Single-Board Computer) Raspberry Pi. This Hadoop cluster provides computing and storage services, the burden of data centers construction is very small because Raspberry Pi is low priced, purchasing the server is much larger than SBC computer on resources, Hadoop running on each node using distributed computing Map/Reduce processing big data. We run pattern recognition on the SBC clusters and the single computer, they are similar in price; we observed their performance, the SBC clusters were better than single computers in performance that was increased by about 20% and increased number of SBC can improve cluster processing speed. In this research experimental data were apparent, and Hadoop was used by Hadoop Distributed File System (HDFS) in the data storage, it has security better than single computer.

Suggested Citation

  • Chao Hsi Huang & Min Hao Chang & I Hsuan Lin, 2016. "Research on Single-Board Computers Clustering the Computing Performance," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 2(5), pages 125-133.
    • Handle: RePEc:apa:ijtess:2016:p:125-133
    DOI: 10.20469/ijtes.2.40001-5
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    References listed on IDEAS

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    1. E. Uma & A. Kannan, 2016. "Self-Aware Message Validating Algorithm for Preventing XMLBased Injection Attacks," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 2(3), pages 60-69.
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    Cited by:

    1. Joon Woo Jeon & Dong-Hyun Kim & Bumsuk Choi & Geonwoo Kim & Yoo-Sung Kim, 2017. "A Construction of Vehicle Image and Ground Truth Database for Developing Vehicle Maker and Model Recognitions," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(6), pages 229-235.

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