Optimal Resource Allocation of Mobile Edge Computing Using Grasshopper Optimization Algorithm

Abstract

In mobile edge computing (MEC), MEC server can enhance mobile devices (MDs) to execute tasks. Due to the limited communication resource of base station and computation resource of MEC server, it is important for resource allocation (RA) problem to consider such factors as the computation resource demands, the data size and the maximum tolerable execution latency of tasks. From a business perspective, high-value tasks need to be performed in a higher priority. This paper proposes an improved grasshopper optimization algorithm (GOA) for the RA problem to maximize the value of executed tasks on the MEC server. The proposed GOA-RA is examined on a series of numerical experiments to evaluate the effects of the channel bandwidth and the clock cycles of the MEC server. Besides, a group of comparison experiments are arranged between GOA-RA, a genetic algorithm (GA) and a discrete particle swarm optimization (PSO) algorithm with different number of MDs. The results demonstrate that the proposed GOA-RA is effective for the RA problem in MEC system.