Abstract:
Mobile edge computing (MEC) has gained popularity due to its low latency, location awareness, and mobility support, which offsets cloud computing’s drawbacks. However, QoS-aware recommendation and composition of MEC services may fail due to dynamically changing QoS, which reduces user satisfaction and negates MEC’s benefits.
We propose two context-aware MEC service QoS prediction schemes that take into account user- and service-related contextual factors and various scheduling scenarios. The first scheme uses two context-aware real-time QoS estimation methods for real-time MEC service scheduling.
One method estimates real-time multi-QoS of MEC services, while the other estimates fitted QoS. The second scheme is for future MEC services. It uses two context-aware QoS prediction methods. One method predicts multi-QoS and the other fitted QoS of MEC services.
Finally, the proposed QoS prediction methods are used to develop adaptive QoS prediction strategies. These strategies can schedule the best QoS prediction method. Our proposed methods are tested extensively.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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