Abstract
<p>In this ever-developing technological world, one way to manage and deliver services 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 increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem 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).</p>
Highlights
Cloud computing provides on-demand services, including networks, servers, storage, and applications through its massive and effective computing paradigm
Proposed algorithms This study focuses on a novel method for task scheduling based on bin packing problem algorithms such as first-fit, next-fit, and best-fit algorithms [28]
Taking the makespan as an objective of our proposed work helped us in the improvement of other metrics such as the average waiting time, the average resource utilisation, and the degree of imbalance (DI):
Summary
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]. Apart from that, task scheduling, which has gained traction nowadays, introduces the option of choosing the resources distribution between various tasks. It should be noted that each workflow or tasks may have scalable scheduling on multiple virtual machines (VMs). Its nondeterministic polynomial time (NP) nature may cause issues that stemmed from the resources’ unstable characteristics and dynamic nature [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
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