Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems

  • Authors:

  • Shuai Ding;Shanlin Yang;Youtao Zhang;Changyong Liang;Chenyi Xia

  • Affiliations:

  • School of Management, Hefei University of Technology, Box 270, Hefei 230009, Anhui, PR China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefe ...;School of Management, Hefei University of Technology, Box 270, Hefei 230009, Anhui, PR China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefe ...;Department of Computer Science, University of Pittsburgh, Pittsburgh 15213, PA, USA;School of Management, Hefei University of Technology, Box 270, Hefei 230009, Anhui, PR China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefe ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300191, PR China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2014

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Abstract

The collection and combination of assessment data in trustworthiness evaluation of cloud service is challenging, notably because QoS value may be missing in offline evaluation situation due to the time-consuming and costly cloud service invocation. Considering the fact that many trustworthiness evaluation problems require not only objective measurement but also subjective perception, this paper designs a novel framework named CSTrust for conducting cloud service trustworthiness evaluation by combining QoS prediction and customer satisfaction estimation. The proposed framework considers how to improve the accuracy of QoS value prediction on quantitative trustworthy attributes, as well as how to estimate the customer satisfaction of target cloud service by taking advantages of the perception ratings on qualitative attributes. The proposed methods are validated through simulations, demonstrating that CSTrust can effectively predict assessment data and release evaluation results of trustworthiness.