Mobile edge computing (MEC) makes up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing QoS always results in failures of QoS-ware recommendation and composition of MEC services, which significantly negates the advantages of MEC. To address this issue, considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One can estimate the real-time multi-QoS of MEC services and the other can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One can predict the multi-QoS of MEC services and the other can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed methods. According to these strategies, the most appropriate QoS prediction method could be scheduled adaptively. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.
- mobile edge computing
- adaptive QoS prediction
- case-based reasoning
- support vector machine
- artificial bee colony algorithm