Bayesian Personalized Ranking-Based Rank Prediction Scheme (BPR-RPS)

Cloud service capabilities can always be clearly indicated by Quality of Service (QoS) properties. The quality and effectiveness of cloud services can be measured at both client and server sides. The parameters such as response time, throughput and failure rate influence the QoS properties at the client side. These parameters may differ with the utilization of the application for the same service. The proposed Bayesian Personalized Ranking-based Rank Prediction Scheme (BPR-RPS) provides rank prediction of QoS properties on the client side, since client-side properties vary with the usage of the application. The proposed rank prediction framework provides ranking to the services when particular cloud services are requested by the client. Personalization of cloud providers and customers is made possible by concentrating on the item recommendation imposed on the cloud by creating user-specific ranking for a set of items. The past response of the user with respect to the item aids in decision-making. The proposed ranking framework includes the following modules: Computation of similarity index between the active and ad hoc users and finding of identical users based on similarity index values.