Load Balancing Aware Task Offloading in Mobile Edge Computing

Abstract

Prompted by the remarkable progress in wireless communications technology and the explosive growth in the number of mobile devices (MDs), there is an increasing need for performing computation-intensive tasks in mobile edge computing. However, limited to the ability of MDs, MDs are difficult to meet the computational demands of the tasks. So how to offload and process computationally intensive tasks is a key problem in the field of mobile edge computing (MEC). In this paper, we propose a load balancing aware task offloading method in mobile edge computing environment, which aims to maximize computing speed and minimize server selection time. Firstly, we model the wireless channel gain and offloading strategy as Markov Decision Process. Secondly, we propose an offloading strategy generation algorithm based on deep reinforcement learning to generate binary offloading strategy. In addition, due to the limited load capacity of edge servers(ESs), we consider how to balance the load of global ESs and forward tasks to other ESs. Therefore, we propose a Particle Swarm Optimization(PSO) algorithm based on server's load to achieve the optimal edge server selection goal. In order to evaluate the effectiveness of the proposed method, we use real data sets to do simulation experiments. The experimental results show that the proposed method can achieve lower latency in comparison with the state-of-the-art approaches applied to similar problems.