International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 4, August 2024, pp. 4714~4720 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4714-4720  4714 Journal homepage: http://ijece.iaescore.com Mean makespan task scheduling approach for the edge computing environment Nisha Saini, Jitender Kumar Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal (Sonipat), India Article Info ABSTRACT Article history: Received Feb 7, 2024 Revised Apr 4, 2024 Accepted Apr 16, 2024 Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics. Keywords: Edge computing Makespan minimization Processing time optimization Task allocation Task scheduling Virtual machines This is an open access article under the CC BY-SA license. Corresponding Author: Nisha Saini Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal-131039, Sonipat, India Email: saini.nisha0203@gmail.com 1. INTRODUCTION The extensive deployment of internet of things (IoT) applications has led to the development of edge computing, offering low latency and fast response times for time-sensitive applications, including healthcare, emergency services, and traffic monitoring. The edge layer integrates edge and cloud resources to offer services and streamline data flow management [1]. The diversity of edge computing necessitates the use of efficient techniques to effectively optimize user requests or workloads. Load balancing is essential in edge computing to handle the growing number of users and effectively handle all user requests [2]. Adequate load balancing is crucial for improving resource utilization [3], minimizing makespan [4], and optimizing the overall performance of edge computing systems [5]. Two approaches for evenly distributing cloud load monitoring are virtual machines (VMs) and task scheduling. In edge computing, task scheduling is considered an NP-hard problem [6] due to the diverse configurations of hosts and VMs, which can quickly adapt to fluctuating user requests. It is very challenging to identify every prospective mapping among tasks and resources in the edge computing paradigm. Consequently, there is an imperative requirement for an efficient task-scheduling technique that prioritizes the strategic allocation of tasks. The goal is to prevent any VM from being underloaded or overloaded, ensuring an evenly distributed workload across all VMs.