CLC number: TP 193

Mobility-Aware and Energy-Efficient Task Offloading Strategy for Mobile Edge Workflows

¹ School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, Hubei, China
² Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan 430205, Hubei, China
³ School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, Hubei, China

^† To whom correspondence should be addressed. E-mail: juanli2018@wit.edu.cn

Received: 10 September 2022

Abstract

With the rapid growth of the Industrial Internet of Things (IIoT), the Mobile Edge Computing (MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service (QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue, this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling (DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.