Research on Task Offloading Strategy for Ultra Dense Edge Computing Network

Abstract

In order to meet the explosive growth of mobile communication service demands for 5G and new Internet of Things applications, mobile edge computing enabled user-centric ultra-dense network is regarded as a promising solution. Task offloading is one of the means to process computation intensive and data intensive tasks effectively. However, when massive mobile users offload computation tasks to edge servers under the constraint of the limited wireless resources, the joint optimization of their offloading decisions becomes prohibitively complex. In this paper, a heuristic task offloading scheme based on binary hybrid grey wolf optimization is proposed for investigating the joint resource allocation and task offloading problem in ultradense edge computing architectures. Numerical simulation results show that the proposed scheme can effectively improve the response rate and system benefit, and perform better as the number of user increases.