QoS Uncertainty Filtering for Fast and Reliable Web Service Selection

How to select the optimal composited service from a set of functionally equivalent services but different QoS attributes has become a hot research in service computing. However existing approaches are inefficient as they search all solution spaces. More importantly, they neglect the QoS inherently uncertainty due to the dynamic network environment. In this paper, we propose a fast and reliable Web service selection approach that attempts to select the best reliable composited service on the basis of filtering low reliable Web services according to the uncertainty of QoS. The approach first employs information theory and variance theory to abandon high QoS uncertainty services and downsize the solution spaces. A reliability fitness function is then designed to select the best reliable service for composited services. We experimented with real-world and synthetic datasets and compared our approach with other approaches. Our results show that our approach is not only fast, but also find more reliable composited services.