Task offloading framework to meet resiliency demand in mobile edge computing system

With the development of 5 G mobile users are increasing massively. Some mobile applications like healthcare are latency-critical and requires real-time data processing. A preference-based task offloading framework in mobile edge computing with a device-to-device offloading (MECD2D) system has been proposed to fulfill the latency demands of such applications for minimum energy consumption ensuring resiliency. The problem is formulated as a constraint-based non-linear optimization problem which is complex. The resources are allocated in two steps. In the first step, resources are allocated based on latency demand to ensure resiliency. In the second step, allocated resources are optimized using a non-cooperative mean field game for dynamic system. To ensure the performance of the system for dynamic network, the results are executed on a real-time Shanghai dataset. The computational results indicate that the proposed algorithm performs better in terms of energy consumption. Other parameters such as throughput, network utilization and task computation are also analysed. The results are verified by performing the proposed algorithm with existing Q learning and mean-field game algorithms. The results performed on the dataset indicate an improvement in energy consumption by 5-10 %, and 10-50 % as compared to Q learning and mean-field game respectively.