Discovering admissibleWeb services with uncertain QoS

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.