Context-aware and Adaptive QoS Prediction for Mobile Edge Computing Services


Mobile edge computing (MEC) has recently gained a significant amount of momentum due to the fact that it permits the utilization of its services with low latency, location awareness, and mobility support. This is done in order to compensate for the drawbacks of Cloud Computing. The dynamically changing quality of service (QoS), on the other hand, may lead to failures of QoS-aware recommendation and composition of MEC services. This results in a significant decrease in the level of satisfaction experienced by users and nullifies the benefits of MEC. In order to address this problem, we have come up with two context-aware QoS prediction schemes for MEC services. These schemes take into account both user-related and service-related contextual factors, as well as a variety of scheduling scenarios involving MEC services. The first strategy is intended for use in circumstances in which MEC services are scheduled in real-time. It is comprised of two context-aware real-time QoS estimation methods. Both of these methods can estimate the real-time quality of service for MEC services, but only one of them can estimate the real-time multi-QoS for MEC services. The other method can estimate the real-time fitted QoS for MEC services. The second plan is tailored to circumstances in which MEC services are to be scheduled at some point in the near or distant future. Two different context-aware quality of service prediction methods are included in this scheme. Both the multi-QoS and the fitted QoS of MEC services can be predicted by one method, while only the fitted QoS can be predicted by the other method. In conclusion, adaptive QoS prediction strategies are developed taking into consideration the characteristics of the QoS prediction methods that have been proposed. These strategies allow for the scheduling of the Quality of Service (QoS) prediction method that is the most appropriate. Extensive testing is carried out in order to validate the approaches that we have proposed and to demonstrate how well they work.

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