Mobile Edge Computing Based Task Offloading and Resource Allocation in Smart Grid

Computational offloading, as one of the means to reduce latency and energy consumption in mobile edge computing (MEC), can reduce industrial costs through reasonable offloading decisions. A mixed-integer nonlinear optimization problem that minimizes task completion time is constructed for the smart grid scenario where there are power terminals with insufficient computing power to deal with high latency arising from low latency and high reliability applications, and a joint optimization strategy for task offloading decision and resource allocation is proposed. The strategy first decomposes the problem into two sub-problems, resource allocation and task offloading, and first adopts the Lagrange multiplier method to obtain the optimal solutions for computing resources as well as spectrum resource allocation, and then uses an adaptive genetic algorithm to formulate the offloading decision under the condition of determining the optimal solution for each resource allocation. The simulation results show that the proposed algorithm can minimize the task latency of electric terminals under the condition of limited resources and improve the service experience of end users.