Title: An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation

Authors: Bantupalli Nagalakshmi; Sumathy Subramanian

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India ' School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India

Abstract: Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employment. So, a multi-objective-based task scheduling for edge computing is suggested in this study. This paper develops the adaptive honey badger optimisation algorithm (AHBA) to accomplish this goal. The lack of population, the original honey badger algorithm (HBO) has the issue of becoming trapped in local optima. To maintain population variety and improve convergence towards the ideal solution, HBO is combined with the opposition-based learning technique (OBL). Based on makespan, cost, energy consumption, and resource usage, the multi-objective function is created. According to simulation results, the proposed approach has a lot of potential in this field. Java and cloud Simulator are used to implement the suggested model.

Keywords: edge computing; cloud computing; task scheduling; honey badger algorithm; HBA; opposition-based learning; OBL; makespan; cost; energy consumption; and resource utilisation.

DOI: 10.1504/IJWET.2024.139866

International Journal of Web Engineering and Technology, 2024 Vol.19 No.2, pp.110 - 126

Received: 30 Sep 2023
Accepted: 29 Dec 2023
Published online: 08 Jul 2024 *

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