International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 3, June 2022, pp. 3226∼3237 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i3.pp3226-3237 ❒ 3226 An efficient cloudlet scheduling via bin packing in cloud computing Amine Chraibi, Said Ben Alla, Abdellah Ezzati Faculty of Science and Technology, Laboratory of Emerging Technologies Monitoring, Hassan First University of Settat, Settat, Morocco Article Info Article history: Received May 10, 2021 Revised Jan 14, 2022 Accepted Jan 31, 2022 Keywords: Bin packing problem Cloud computing CloudSim Particle swarm optimisation algorithm Task scheduling ABSTRACT In this ever-developing technological world, one way to manage and deliver ser- vices is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and in- crease profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling prob- lem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS). This is an open access article under the CC BY-SA license. Corresponding Author: Said Ben Alla Department of Mathematics and Computer Science, Hassan First University of Settat 577 Casablanca Road, Settat, Morocco Email: saidb_05@hotmail.com 1. INTRODUCTION Cloud computing provides on-demand services, including networks, servers, storage, and applications through its massive and effective computing paradigm. The National Institute of Standards and Technology (NIST) defines it as a developing technology that frequently offers accessible and on-demand network access to shared computing resources [1]. The typical models of the cloud are: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [1]. Apart from that, task scheduling, which has gained traction nowadays, introduces the option of choos- ing the resources distribution between various tasks. It should be noted that each workflow or tasks may have scalable scheduling on multiple virtual machines (VMs). Regarding task scheduling, its nondeterministic polynomial time (NP) nature may cause issues that stemmed from the resources’ unstable characteristics and dynamic nature [2]. In the process, the task scheduler accepts the queued tasks from the users and assigns the tasks to available resources based on the task resources parameters [2]. The research problem is to improve task scheduling in cloud computing by reducing the execution time of queuing tasks and enhancing the use of resources. Journal homepage: http://ijece.iaescore.com