Discovering admissible Web services with uncertain QoS

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• 作者：Xiaodong Fu (1)
  Kun Yue (2)
  Li Liu (1)
  Ping Zou (3)
  Yong Feng (1)
  
  1. Yunnan Provincial Key Laboratory of Computer Technology Application ; Faculty of Information Engineering and Automation ; Kunming University of Science and Technology ; Kunming ; 650500 ; China
  2. School of Information Science and Engineering ; Yunnan University ; Kunming ; 650091 ; China
  3. Faculty of Management and Economics ; Kunming University of Science and Technology ; Kunming ; 650093 ; China
  
• 关键词：Web services ; uncertain QoS ; partial preference ; empirical distribution function ; stochastic dominance ; admissible set
• 刊名：Frontiers of Computer Science in China
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：9
• 期：2
• 页码：265-279
• 全文大小：636 KB
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• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

Open and dynamic environments lead to inherent uncertainty of Web service QoS (Quality of Service), and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empirical distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services including alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance (SD) rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported progressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be improved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scalability of the proposed algorithm.