Title: A novel dual-purpose metaheuristic based MCDM model with an optimal task scheduling algorithm for cloud computing

Authors: Malatesh Kamatar; Bindhu P. Madhavi

Addresses: Department of Computer Science and Engineering, PDIT, Hosapete, India ' Department of AI and ML, The Oxford College of Engineering, Bangalore, India

Abstract: The research focuses on the main issues that arise in cloud computing regarding how to schedule tasks and pick the right virtual machines due to growing need for virtual resources. Therefore, an innovative task scheduling model is proposed by integrating multi-criteria decision making (MCDM) and the shortest job first (SJF) technique. An approach using VIKOR in MCDM is followed to highlight tasks by their queuing, task and resource importance, thereby cutting down waiting time. If priorities are equal, SJF algorithm prefers to select shorter tasks to ensure no conflicts occur. An original metaheuristic algorithm called chaotic artificial bee colony with quantum (CABCQ) is proposed to ensure best choice of VMs by considering the time needed to execute, transfer and process tasks. Cloud task assignment and VM usage improve due to the models, which boosts Python CloudSim's performance in terms of makespan, latency, user priority, energy consumption, computation time and cost.

Keywords: cloud computing; task scheduling; multi-criteria decision making; MCDM; shortest job first; SJF; chaotic artificial bee colony with quantum algorithm; CABCQ.

DOI: 10.1504/IJCC.2025.148725

International Journal of Cloud Computing, 2025 Vol.14 No.3, pp.327 - 352

Received: 02 Oct 2024
Accepted: 05 Mar 2025
Published online: 21 Sep 2025 *

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