Narrow Results

Selecting the optimal service from a mass of functionally equivalent services with low time cost is significant. Previous research addressed this issue excessively relying on several kinds of optimization algorithms. In this work, we propose an approach that prunes redundant services and reduces search space of service selection on the basis of Skyline computing and context inference. We firstly adopt Skyline computing to prune redundant services. Then, we present the notion of context service based on context inference to further reduce the size of service selection problem. Finally, mixed integer programming is employed to find the optimal service from context services according to users' Quality of Service (QoS) requirements. Experimental results on a test bed indicate that our approach can find the optimal service with low time cost.