Energy-efficient Offloading Policy for Resource Allocation in Distributed Mobile Edge Computing

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

I. Introduction

Mobile user equipment (UEs), such as smartphones and laptops, are proliferating with the technological evolutions. More and more computation-intensive applications are being rapidly applied on those UEs. Despite the increase of the computing capacity of the UEs, the UEs cannot deal with the tasks that need a lot of computational resources with a large amount of battery energy in a short period [1]. Mobile cloud computing (MCC), which can provide powerful computing and storage resources for UEs to complete computation tasks, is a paradigm to handle the energy-intensive computational tasks [2]. By offloading energy-intensive computational tasks to mobile clouds, MCC can provide UEs with cloud computing services, reduce battery consumption of UEs, and improve user experience. However, if the MCC servers are far away from UEs, they may not be able to satisfy the delay requirement of users.