MA Hua, HU Zhigang, CAI Meiling. Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers[J]. Chinese Journal of Electronics, 2017, 26(6): 1177-1183. doi: 10.1049/cje.2017.09.034
Citation: MA Hua, HU Zhigang, CAI Meiling. Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers[J]. Chinese Journal of Electronics, 2017, 26(6): 1177-1183. doi: 10.1049/cje.2017.09.034

Trustworthy Service Selection Integrating Cloud Model and Possibility Degree Ranking of Interval Numbers

doi: 10.1049/cje.2017.09.034
Funds:  This work is supported by the National Natural Science Foundation of China (No.61572525) and Science and Technology Project of Hunan Province (No.2014FJ3122).
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  • Corresponding author: HU Zhigang (corresponding author) was born in 1963. He received his B.S. degree and his M.S. degree from Central South University, China in 1985 and in 1988, and received his Ph.D. degree from Central South University, China in 2002.(
  • Received Date: 2015-08-10
  • Rev Recd Date: 2015-10-14
  • Publish Date: 2017-11-10
  • It has been challenging to select suitable services from abundant candidates in cloud environment. Aiming to the characteristics of batch computing mode and stream computing mode, a novel trustworthy service selection approach is proposed integrating cloud model and interval numbers theory. To facilitate potential users to understand the quality of service, the trustworthiness of service is described with interval number using reverse cloud generator, and the services with poor performance are filtered out by employing deviation degree or proximity degree. Two formulas of possibility degree of interval numbers are designed to compare the trustworthiness values between cloud services by utilizing probability zone analysis and geometrical analysis respectively, and the ranking method for possibility degree of interval numbers is exploited to select the most trustworthy service. The experiments show that this approach is effective to improve the accuracy of service selection and select trustworthy service for potential users in cloud paradigm.
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