Computation Offloading Strategy in Cloud-Assisted Mobile Edge Computing

Abstract

Mobile Edge Computing(MEC) reduces delay and energy consumption by migrating computing resources to network edge.Compared with cloud computing,edge computing has limited computing resources and cannot meet the needs of all mobile services.To address the problems,this paper proposes a computation offloading strategy for cloud-assisted mobile edge computing.The mobile service is modeled as a workflow model with a priority constraint relationship to analyze the delay and energy consumption during system operation.Then,with minimizing the total system cost(weighted sum of delay and energy consumption) as research objective,a computation offloading algorithm is designed on the basis of improved Genetic Algorithm(GA),of which the operations of coding,crossover,and mutation are partially modified.Simulation results show that compared with the All-Local algorithm,the Random algorithm,the ECGA algorithm,the total system cost of the proposed algorithm is the smallest of existing algorithms.