Email this article Dynamic Orthogonal Particle Swarm Optimization Task Scheduling Algorithm for Cloud Computing Environment
Abstract
Task scheduling remains a critical bottleneck in cloud computing due to its NP-hard complexity, often leading to inefficient resource utilization, high costs, and poor Quality of Service (QoS). While conventional heuristics lack scalability and popular metaheuristics like PSO, GA, and ACO struggle with slow convergence and local optima, this study introduces a Dynamic Orthogonal Particle Swarm Optimization (DOPSO) algorithm that integrates PSO’s global search capability with the Taguchi Orthogonal method for enhanced local search efficiency. Implemented in CloudSim and tested on real-world benchmark datasets (HPC2N, SDSC-SP2, NASA Ames), DOPSO demonstrated significant improvements—reducing makespan, execution cost, and task execution time by up to 21.7%, 18.3%, and 15.9% respectively—over baseline algorithms. The approach also exhibited strong scalability under large workloads, with statistical validation (Kruskal–Wallis H test, p < 0.05) confirming the significance of its performance gains. Overall, DOPSO emerges as a robust, scalable, and multi-objective scheduling framework that not only optimizes time and cost but also aligns with QoS demands, offering promising applicability to future cloud, fog, and energy-aware scheduling contexts.
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Chai, X. (2020). Task scheduling based on swarm intelligence algorithms in high performance computing environment. Journal of Ambient Intelligence and Humanized Computing, 14(11), 14807–14815. https://doi.org/10.1007/s12652-020-01994-0
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Hai, T., Zhou, J., Jawawi, D., Wang, D., Oduah, U., Biamba, C., & Jain, S. K. (2023). Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes. Journal of Cloud Computing, 12(1). https://doi.org/10.1186/s13677-022-00374-7
Malti, A. N., Benmammar, B., & Hakem, M. (2023). A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing. Lecture Notes in Networks and Systems, 593 LNNS, 263–278. https://doi.org/10.1007/978-3-031-18516-8_19
Muniswamy, S., & Vignesh, R. (2022). DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00304-7
Tamilarasu, P., & Singaravel, G. (2024). Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment. Journal of Engineering Research (Kuwait), June. https://doi.org/10.1016/j.jer.2023.09.024
Yin, L., Sun, C., Gao, M., Fang, Y., Li, M., & Zhou, F. (2023). Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing. Intelligent Automation and Soft Computing, 37(2), 1587–1608. https://doi.org/10.32604/iasc.2023.039380
Chai, X. (2020). Task scheduling based on swarm intelligence algorithms in high performance computing environment. Journal of Ambient Intelligence and Humanized Computing, 14(11), 14807–14815. https://doi.org/10.1007/s12652-020-01994-0
Chandrashekar, C., Krishnadoss, P., Kedalu Poornachary, V., Ananthakrishnan, B., & Rangasamy, K. (2023). HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing. Applied Sciences (Switzerland), 13(6). https://doi.org/10.3390/app13063433
Du, L., & Wang, Q. (2024). Metaheuristic Optimization for Dynamic Task Scheduling in Cloud Computing Environments. International Journal of Advanced Computer Science and Applications, 15(7), 590–597. https://doi.org/10.14569/IJACSA.2024.0150758
Dubey, K., & Sharma, S. C. (2023). A hybrid multi-faceted task scheduling algorithm for cloud computing environment. International Journal of System Assurance Engineering and Management, 14(March), 774–788. https://doi.org/10.1007/s13198-021-01084-0
Hai, T., Zhou, J., Jawawi, D., Wang, D., Oduah, U., Biamba, C., & Jain, S. K. (2023). Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes. Journal of Cloud Computing, 12(1). https://doi.org/10.1186/s13677-022-00374-7
Malti, A. N., Benmammar, B., & Hakem, M. (2023). A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing. Lecture Notes in Networks and Systems, 593 LNNS, 263–278. https://doi.org/10.1007/978-3-031-18516-8_19
Muniswamy, S., & Vignesh, R. (2022). DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00304-7
Tamilarasu, P., & Singaravel, G. (2024). Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment. Journal of Engineering Research (Kuwait), June. https://doi.org/10.1016/j.jer.2023.09.024
Yin, L., Sun, C., Gao, M., Fang, Y., Li, M., & Zhou, F. (2023). Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing. Intelligent Automation and Soft Computing, 37(2), 1587–1608. https://doi.org/10.32604/iasc.2023.039380
Chai, X. (2020). Task scheduling based on swarm intelligence algorithms in high performance computing environment. Journal of Ambient Intelligence and Humanized Computing, 14(11), 14807–14815. https://doi.org/10.1007/s12652-020-01994-0
Chandrashekar, C., Krishnadoss, P., Kedalu Poornachary, V., Ananthakrishnan, B., & Rangasamy, K. (2023). HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing. Applied Sciences (Switzerland), 13(6). https://doi.org/10.3390/app13063433
Du, L., & Wang, Q. (2024). Metaheuristic Optimization for Dynamic Task Scheduling in Cloud Computing Environments. International Journal of Advanced Computer Science and Applications, 15(7), 590–597. https://doi.org/10.14569/IJACSA.2024.0150758
Dubey, K., & Sharma, S. C. (2023). A hybrid multi-faceted task scheduling algorithm for cloud computing environment. International Journal of System Assurance Engineering and Management, 14(March), 774–788. https://doi.org/10.1007/s13198-021-01084-0
Hai, T., Zhou, J., Jawawi, D., Wang, D., Oduah, U., Biamba, C., & Jain, S. K. (2023). Task scheduling in cloud environment: optimization, security prioritization and processor selection schemes. Journal of Cloud Computing, 12(1). https://doi.org/10.1186/s13677-022-00374-7
Malti, A. N., Benmammar, B., & Hakem, M. (2023). A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing. Lecture Notes in Networks and Systems, 593 LNNS, 263–278. https://doi.org/10.1007/978-3-031-18516-8_19
Muniswamy, S., & Vignesh, R. (2022). DSTS: A hybrid optimal and deep learning for dynamic scalable task scheduling on container cloud environment. Journal of Cloud Computing, 11(1). https://doi.org/10.1186/s13677-022-00304-7
Tamilarasu, P., & Singaravel, G. (2024). Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment. Journal of Engineering Research (Kuwait), June. https://doi.org/10.1016/j.jer.2023.09.024
Yin, L., Sun, C., Gao, M., Fang, Y., Li, M., & Zhou, F. (2023). Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing. Intelligent Automation and Soft Computing, 37(2), 1587–1608. https://doi.org/10.32604/iasc.2023.039380.
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