Title: Improved task scheduling strategy for balancing resource utilisation and service quality in mobile edge computing environment

Authors: Michael Pendo John Mahenge

Addresses: Department of Informatics and Information Technology, College of Natural and Applied Sciences, Sokoine University of Agriculture, P.O. Box 3038, Chuo Kikuu, Morogoro, Tanzania

Abstract: The rapid growth of resource-hungry and time-critical applications reflects the rise of resources needed for communication, processing, and energy consumption. Mobile edge computing (MEC) that offers cloud-computing services proximate to users at the edge of mobile network is considered to be the key technology to facilitate task scheduling closer to data-sources. The objective of this paper is to propose an improved task scheduling strategy that selects the best MEC server to process each task, which reduces the system energy consumption and delay. Therefore, we proposed an improved task scheduling strategy on the basis of non-dominated sorting genetic algorithm II (NSGA-II). To improve the performance of NSGA-II, we proposed a hierarchical search policy (NSGA-H) that eliminates the number of redundant comparisons and thus, enhances time complexity. The simulation results illustrate that the proposed strategy improves average service delay, service time, energy consumption, and system utility compared to baseline approaches.

Keywords: mobile edge computing; MEC; task scheduling; quality of experience; QoE; resource-intensive tasks; non-dominated sorting genetic algorithm II; NSGA II.

DOI: 10.1504/IJCC.2024.140489

International Journal of Cloud Computing, 2024 Vol.13 No.4, pp.305 - 329

Received: 09 May 2022
Accepted: 25 Jun 2023
Published online: 20 Aug 2024 *

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