Service recommendation based on parallel graph computing

With the development of cloud service technologies, the amount of services grows rapidly, leading to building high-quality services an urgent and crucial research problem. Service users should evaluate QoS to select the optimal cloud services from a series of functionally equivalent service candidates, because QoS performance of services is varying over time. The reason is that QoS is related to the service overload and network environments. This phenomenon makes QoS prediction for users located in different places even harder. Furthermore, since service invocations are charged by service providers, it is impractical to let users invoke required cloud services to evaluate quality with respect to time and resources. To solve this problem, this paper proposes a cloud service QoS prediction method, called TPP (Time-aware and Parallel Prediction), to provide time-aware and parallel QoS value prediction for various service users. TPP is able to predict without additional invocation of cloud services, since it uses past cloud service usage experience from different service users. We propose and implement tensor decomposition algorithm on the Spark system. The results of extensive experimental show the accuracy and efficiency of TPP.