Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing
Cloud computing delivers on-demand services and scalable computing power in near real-time, redefining modern computing paradigms. Effective task scheduling remains a critical challenge due to dynamic and heterogeneous workloads, directly influencing energy efficiency, response time, and resource ut...
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-05-01
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| Series: | Journal of Applied Science and Engineering |
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| Online Access: | http://jase.tku.edu.tw/articles/jase-202512-28-12-0006 |
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| author | Longyang Du Qingxuan Wang |
| author_facet | Longyang Du Qingxuan Wang |
| author_sort | Longyang Du |
| collection | DOAJ |
| description | Cloud computing delivers on-demand services and scalable computing power in near real-time, redefining modern computing paradigms. Effective task scheduling remains a critical challenge due to dynamic and heterogeneous workloads, directly influencing energy efficiency, response time, and resource utilization. The present research presents an enhanced Particle Swarm Optimization (PSO) algorithm inspired by specific
hummingbird flight characteristics, chosen for their exceptional agility and efficiency. Five hummingbirdinspired concepts are integrated into PSO: incremental position updates to enhance convergence accuracy, stepwise position changes to avoid local optima, energy-conserving movements reducing computational
overhead, decentralized exploration to maintain diversity, and multidirectional searches enhancing solution coverage. Comparative experiments conducted on synthetic and real-world datasets (HPC2N) with diverse task loads demonstrate measurable performance improvements, including up to 18% better resource utilization, up to a 35% decrease in imbalance degree, and up to a 20% improvement in execution cost compared to recent
algorithms. These results confirm that each hummingbird-inspired concept distinctly contributes to overcoming conventional PSO limitations, significantly enhancing exploration ability, convergence speed, load balancing, and adaptability to diverse cloud computing scenarios. |
| format | Article |
| id | doaj-art-af55ce895a9d47ca97a753769a28c98a |
| institution | Kabale University |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-af55ce895a9d47ca97a753769a28c98a2025-08-20T03:53:52ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-05-0128122373238310.6180/jase.202512_28(12).0006Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud ComputingLongyang Du0Qingxuan Wang1School of Artificial Intelligence, Jiaozuo University, Jiaozuo 454000, Henan, ChinaSchool of Artificial Intelligence, Jiaozuo University, Jiaozuo 454000, Henan, ChinaCloud computing delivers on-demand services and scalable computing power in near real-time, redefining modern computing paradigms. Effective task scheduling remains a critical challenge due to dynamic and heterogeneous workloads, directly influencing energy efficiency, response time, and resource utilization. The present research presents an enhanced Particle Swarm Optimization (PSO) algorithm inspired by specific hummingbird flight characteristics, chosen for their exceptional agility and efficiency. Five hummingbirdinspired concepts are integrated into PSO: incremental position updates to enhance convergence accuracy, stepwise position changes to avoid local optima, energy-conserving movements reducing computational overhead, decentralized exploration to maintain diversity, and multidirectional searches enhancing solution coverage. Comparative experiments conducted on synthetic and real-world datasets (HPC2N) with diverse task loads demonstrate measurable performance improvements, including up to 18% better resource utilization, up to a 35% decrease in imbalance degree, and up to a 20% improvement in execution cost compared to recent algorithms. These results confirm that each hummingbird-inspired concept distinctly contributes to overcoming conventional PSO limitations, significantly enhancing exploration ability, convergence speed, load balancing, and adaptability to diverse cloud computing scenarios.http://jase.tku.edu.tw/articles/jase-202512-28-12-0006optimizationtask schedulingcloud computinghummingbird flight |
| spellingShingle | Longyang Du Qingxuan Wang Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing Journal of Applied Science and Engineering optimization task scheduling cloud computing hummingbird flight |
| title | Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing |
| title_full | Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing |
| title_fullStr | Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing |
| title_full_unstemmed | Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing |
| title_short | Hummingbird-Inspired Modified Particle Swarm Optimization for Efficient Task Scheduling in Cloud Computing |
| title_sort | hummingbird inspired modified particle swarm optimization for efficient task scheduling in cloud computing |
| topic | optimization task scheduling cloud computing hummingbird flight |
| url | http://jase.tku.edu.tw/articles/jase-202512-28-12-0006 |
| work_keys_str_mv | AT longyangdu hummingbirdinspiredmodifiedparticleswarmoptimizationforefficienttaskschedulingincloudcomputing AT qingxuanwang hummingbirdinspiredmodifiedparticleswarmoptimizationforefficienttaskschedulingincloudcomputing |