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
Cloud computing has become a basic part of modern IT services. Job scheduling remains a key technical issue because it influences performance, cost, and resource use. Traditional static and heuristic schedulers often struggle with the dynamic, heterogeneous, and multi-objective nature of current cloud environments. This has encouraged the use of metaheuristic methods. This study presents a systematic empirical comparison of two algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO)) for cloud job scheduling. Both algorithms were implemented in a Cloud Sim simulation environment. We used Planet Lab-style workload traces and EC2-like heterogeneous VM catalogs to support repeatable experiments. The evaluation considered Make span, Flowtime, Total Cost, and Average CPU Utilization in a baseline setup (512 tasks, 8 VMs). In the other tests, the number of tasks was increased to 2,000. Under these conditions, the PSO results were more acceptable. Compared with GWO, mean completion time was reduced by about 39.11%. Average CPU utilization increased by 16.21%. Total cost showed a modest decrease of 4.11%. PSO generally was able to produce schedules within fewer iterations. On the other hand, GWO showed a different pattern. When larger populations or longer runtimes were available, it appeared more suitable for deeper exploration. Overall, the study provides a reproducible methodology, a documented data pipeline, and a set of empirical benchmarks.
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Ababneh, J. (2021). A hybrid approach based on grey wolf and whale optimization algorithms for solving cloud task scheduling problem. MATHEMATICAL PROBLEMS IN ENGINEERING, *2021*(1), 3517145.
Abed-Alguni, B. H., & Alawad, N. A. (2021). Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments. APPLIED SOFT COMPUTING, *102*, 107113.
Agarwal, M., & Srivastava, G. M. S. (2021). Opposition-based learning inspired particle swarm optimization (OPSO) scheme for task scheduling problem in cloud computing. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, *12*(10), 9855-9875.
Aktan, M. N., & Bulut, H. (2022). Metaheuristic task scheduling algorithms for cloud computing environments. CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE, *34*(9), e6513.
Al-Wesabi, F. N., Obayya, M., Hamza, M. A., Alzahrani, J. S., Gupta, D., & Kumar, S. (2022). Energy aware resource optimization using unified metaheuristic optimization algorithm allocation for cloud computing environment. SUSTAINABLE COMPUTING: INFORMATICS AND SYSTEMS, *35*, 100686.
Anbarkhan, S. H., & Rakrouki, M. A. (2023). An enhanced PSO algorithm for scheduling workflow tasks in cloud computing. ELECTRONICS, *12*(12), 2580.
Choudhary, A., & Rajak, R. (2024). A novel strategy for deterministic workflow scheduling with load balancing using modified min-min heuristic in cloud computing environment. CLUSTER COMPUTING, *27*(5), 6985-7006.
CloudSim Plus. (2022). CloudSim Plus whitepaper and API documentation describing PlanetLab utilities and workload ingestion. Retrieved from https://cloudsimplus.github.io/cloudsimplus-whitepaper/
Cui, M., & Wang, Y. (2025). An Effective QoS-Aware Hybrid Optimization Approach for Workflow Scheduling in Cloud Computing. SENSORS, *25*(15), 4705.
Fu, X., Sun, Y., Wang, H., & Li, H. (2023). Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm. CLUSTER COMPUTING, *26*(5), 2479-2488.
Gupta, S., Deep, K., Moayedi, H., Foong, L. K., & Assad, A. (2021). Sine cosine grey wolf optimizer to solve engineering design problems. ENGINEERING WITH COMPUTERS, *37*(4), 3123-3149.
Huang, X., Xie, M., An, D., Su, S., & Zhang, Z. (2024). Task scheduling in cloud computing based on grey wolf optimization with a new encoding mechanism. PARALLEL COMPUTING, *122*, 103111.
Khan, M. A., & ur Rasool, R. (2024). A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, *187*, 104847.
Makhadmeh, S. N., Al-Betar, M. A., Doush, I. A., Awadallah, M. A., Kassaymeh, S., Mirjalili, S., & Zitar, R. A. (2023). Recent advances in Grey Wolf Optimizer, its versions and applications. IEEE ACCESS, *12*, 22991-23028.
Mangalampalli, S., Karri, G. R., & Kumar, M. (2023). Multi objective task scheduling algorithm in cloud computing using grey wolf optimization. CLUSTER COMPUTING, *26*(6), 3803-3822.
Menaka, M., & Kumar, K. S. (2024). Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing. INTERNATIONAL JOURNAL OF COGNITIVE COMPUTING IN ENGINEERING, *5*, 192-198.
Nabi, S., Ahmad, M., Ibrahim, M., & Hamam, H. (2022). AdPSO: adaptive PSO-based task scheduling approach for cloud computing. SENSORS, *22*(3), 920.
Potluri, S., Hamad, A. A., Godavarthi, D., & Basa, S. S. (2023). Enhanced Task Scheduling Using Optimized Particle Swarm Optimization Algorithm in Cloud Computing Environment. ICST TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 1-5.
Pradhan, A., Bisoy, S. K., & Das, A. (2022). A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, *34*(8), 4888-4901.
Prasad, R., Roy, A., & Kumari, S. (2025). Enhancing Cloud Task Scheduling Using a Hybrid Particle Swarm and Grey Wolf Optimization Approach. ARXIVPREPRINT ARXIV:2505.15171.
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S. (2022). Particle swarm optimization: A comprehensive survey. IEEE ACCESS, *10*, 10031-10061.
Singh, G., & Chaturvedi, A. K. (2021, May). Particle swarm optimization-based approaches for cloud-based task and workflow scheduling: a systematic literature review. In 2021 2ND INTERNATIONAL CONFERENCE ON SECURE CYBER COMPUTING AND COMMUNICATIONS (ICSCCC) (pp. 350-358). IEEE.
Verma, S., & Bala, A. (2024). Etsa-lp: Ensemble time-series approach for load prediction in cloud. COMPUTING AND INFORMATICS, *43*(1), 64-93.
Vispute, S. D., & Vashisht, P. (2023). Energy-efficient task scheduling in fog computing based on particle swarm optimization. SN COMPUTER SCIENCE, *4*(4), 391.
Wang, H., Zhang, J., Fan, J., Zhang, C., Deng, B., & Zhao, W. (2025). An improved grey wolf optimizer with flexible crossover and mutation for cluster task scheduling. INFORMATION SCIENCES, *704*, 121943.
Zambuk, F. U., Gital, A. Y. U., Jiya, M., Gari, N. A. S., Ja'afaru, B., & Muhammad, A. (2021). Efficient task scheduling in cloud computing using
multi-objective hybrid ant colony optimization algorithm for energy efficiency.
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Copyright (c) 2026 Dunia Ameen Abd Al-sahib, Zainb Hassan Radhy, Dhuha Taima Al-Dawoodi
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