A Clustering-Based QoS Prediction Approach for Web Service Selection

With the number increasing of Web services, recommending and selecting the optimal Web services for consumers has become one of the most important challenges in the field of service computing. The goal of consumers is to discover and use services which lead to them experiencing the highest quality. The existing approaches of quality evaluation mostly assume the primary goal of consumer as being optimization of performance, so that the consumers are unable to effectively identify and engage with providers who deliver services that will best meet their needs. In order to solve this problem, we propose a clustering-based QoS prediction framework for Web services. In our framework, we employ the k-means clustering algorithm. We determinate the similarity among consumers based on their expectations. In terms of the relationship between their expectations and the rating of the services, we predict the rating of a service used to select desirable services. At last, the experiment shows that the approach achieves better prediction.