A Learning Approach to QoS Prediction via Multi-Dimensional Context

Advances in mobile Internet technology have enabled the clients of Web services to be able to adjust to context changes, which need to rely on monitoring to QoS of Web services. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the complicated relations among multi-dimension of context. This paper proposes a learning approach to quality-of-service (QoS) prediction of web services via multi-dimensional context derived from the past invocation history. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.