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A novel cluster computing technique based on signal clustering and analytic hierarchy model using hadoop

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Abstract

The rapid growth of Internet has vast amounts of information over online. The correct information can be provided by the source only if the information is processed, analyzed and linked. The efficient store and manage model is required to access and to protect these large data. These data are structured and unstructured which is available in online in order to process such data an intense technology is required. The cloud computing satisfies the need of store and manage model. Whereas to access and protect data, many intense technologies like parallel and map reduce methods are available. But these methods face difficulties in large data processing. In this article these traditional difficulties are overcome by Hadoop data model to give a high performance computing of large data in cloud computing environment. In the experiment, the proposed method was compared with previous works. As a result, the proposed method achieved 0.51 Packet delivery ratio with 0.71s Elapsed Time/Word transfer at the Receiver throughput of 770kbps, which is much better than that of previous work. The single cluster and analytical hierarchy process (AHP) is used to compute data along with Hadoop to provide fault tolerance over failures, less processing time and communication errors.

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Acknowledgements

This work is financially supported by the scientific research program of Shanxi Provincial Education Department (Program No. 16JK2236).

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Authors and Affiliations

  1. Xijing University, Xi’an, 710123, China

    Wenzhun Huang, Yucheng Zhang & Shanwen Zhang

  2. GoPerception Laboratory, Ithaca, NY, 14850, USA

    Haoxiang Wang

  3. Cornell University, Ithaca, NY, 14853, USA

    Haoxiang Wang

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  1. Wenzhun Huang
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  3. Yucheng Zhang
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  4. Shanwen Zhang
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Correspondence to Haoxiang Wang.

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Huang, W., Wang, H., Zhang, Y. et al. A novel cluster computing technique based on signal clustering and analytic hierarchy model using hadoop. Cluster Comput 22 (Suppl 6), 13077–13084 (2019). https://doi.org/10.1007/s10586-017-1205-9

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  • DOI: https://doi.org/10.1007/s10586-017-1205-9

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