Title: An enhanced genetic algorithm for computation task offloading in MEC scenario

Authors: Jiacheng Zhao; Wenzao Li; Hantao Liu; Peizhen Yu; Hanyun Li; Zhan Wen

Addresses: School of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China ' Chengdu University of Information Technology, Chengdu, Sichuan, China; Network and Data Security Key Lab of Sichuan Pro., University of Electronic Science and Technology of China, Chengdu, Sichuan, China ' Educational Informationisation and Big Data Centre, Education Department of Sichuan Province, Chengdu, Sichuan, China ' School of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China ' School of Communication Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China ' Chengdu University of Information Technology, Chengdu, Sichuan, China

Abstract: The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm.

Keywords: task offloading; genetic algorithm; bandwidth constraint; NP-hard; dense terminal distribution; offloading strategy; mobile edge computing; 5G; energy-efficient; low latency.

DOI: 10.1504/IJWMC.2023.133059

International Journal of Wireless and Mobile Computing, 2023 Vol.25 No.2, pp.118 - 127

Received: 12 Jan 2022
Received in revised form: 04 Jul 2022
Accepted: 07 Jul 2022
Published online: 29 Aug 2023 *

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