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A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback

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Abstract

With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency.

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  1. https://snap.stanford.edu/data/web-Amazon.

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Acknowledgements

We thank the two anonymous reviewers for their detailed constructive comments on the preliminary versions of the paper, which enabled us to improve the paper.

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Authors and Affiliations

  1. Department of Information Technology, A.V.C. College of Engineering, Mayiladuthurai, Tamilnadu, India

    Rajganesh Nagarajan

  2. School of Information Technology and Engineering, VIT University, Vellore, Tamilnadu, India

    Ramkumar Thirunavukarasu

  3. Department of Computer Applications, A.V.C. College of Engineering, Mayiladuthurai, Tamilnadu, India

    Selvamuthukumaran Shanmugam

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  1. Rajganesh Nagarajan
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  2. Ramkumar Thirunavukarasu
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  3. Selvamuthukumaran Shanmugam
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Correspondence to Rajganesh Nagarajan.

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Nagarajan, R., Thirunavukarasu, R. & Shanmugam, S. A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback. Int. J. Fuzzy Syst. 20, 339–347 (2018). https://doi.org/10.1007/s40815-017-0347-5

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