Resource Allocation Strategy for MEC System Based on VM Migration and RF Energy Harvesting

Aiming at the influence of user equipment (UE) mobility on computational results feedback of mobile edge computing (MEC) server, a resource allocation strategy based on virtual machine migration is proposed to maximize the system energy efficiency (EE). UEs can harvest radio frequency energy from multiple frequency bands as they move. When the UE moves over a long distance, virtual machine migration is adopted to transfer the computational tasks offloaded by the UE from the initial MEC server to the current MEC server. After the current MEC server completes the computational task, it can directly feed the computational results back to the UE, which can significantly reduce the delay. After the current MEC server completes the computational task, it will feed the computational results back to the UE. By jointly considering offloading computational task by the UE and computational results feedback of MEC server, the problem of power and subcarrier allocation is modeled as a mixed integer nonlinear programming problem. The goal is to maximize the system EE under the constraints of energy consumption, subcarrier allocation, and transmitting power. The suboptimal solution is obtained by introducing the genetic algorithm. Simulation results show that the proposed method has higher EE than partial power or subcarrier allocation methods based on the genetic algorithm.