A Mathematical Modeling and Comparative Analysis of PSO and GWO for Efficient Cloud Job Scheduling
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12598Keywords:
Job scheduling; Particle Swarm Optimization; Grey Wolf Optimization; Cloud computing; Metaheuristic comparison.Abstract
The use of cloud computing has been established as a necessity in modern IT services. Job scheduling is an important technical problem since it affects performance, cost, and resource utilization. Static and heuristic approaches have been known to be inefficient in dynamic, heterogeneous, and multi-objective cloud environments, hence the application of meta-heuristics. The work provides a comparative analysis of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) approaches for cloud job scheduling using Cloud Sim simulation environment. Planet Lab-style workload traces and EC2-like heterogeneous VM catalogs were used for the experimentations. The evaluation of PSO and GWO algorithms was done in terms of Make Span, Flowtime, Total Cost, and Average CPU Utilization in a setup of 512 tasks and 8 VMs. For the rest of the experiments, the number of tasks was increased to 2,000. Under such circumstances, PSO provided better results compared to GWO, with a reduction in mean completion time by approximately 39.11%. The average CPU usage improved by 16.21%. The cost reduced slightly by 4.11%. PSO was normally capable of scheduling using fewer iterations. However, GWO exhibited a totally different performance trend. Where large population sizes or long runtime were available, it seemed to be appropriate for more exploration. In summary, the research has provided a reproducible methodology, data pipeline, and benchmarks.
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Copyright (c) 2026 Dunia Ameen Abd Al-sahib, Zainb Hassan Radhy, Dhuha Taima Al-Dawoodi
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