Cloud Service Recommendation Based on a Correlated QoS Ranking Prediction

Abstract

Quality-of-Service (QoS) is an important concept for service selection and user satisfaction in cloud computing. So far, service recommendation in the cloud is done by means of QoS, ranking and rating techniques. The ranking methods perform much better, when compared with the rating methods. In view of the fact that the ranking methods directly predict QoS rankings as accurately as possible, in most of the ranking methods, an individual QoS value alone is employed to predict the cloud rank. In this paper, we propose a correlated QoS ranking algorithm along with a data smoothing technique and combined with QoS to predict a personalized ranking for service selection by an active user. Experiments are conducted employing a WSDream-QoS dataset, including 300 distributed users and 500 real world web services all over the world. Six different techniques of correlated QoS ranking schemes have been proposed and evaluated. The experimental results showed that this approach improves the accuracy of ranking prediction when compared to a ranking prediction framework using a single QoS parameter.