A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques
Abstract Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO)...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96364-1 |
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| author | Umesh Kumar Lilhore Sarita Simaiya Yogendra Narayan Prajapati Anjani Kumar Rai Ehab Seif Ghith Mehdi Tlija Tarik Lamoudan Abdelaziz A. Abdelhamid |
| author_facet | Umesh Kumar Lilhore Sarita Simaiya Yogendra Narayan Prajapati Anjani Kumar Rai Ehab Seif Ghith Mehdi Tlija Tarik Lamoudan Abdelaziz A. Abdelhamid |
| author_sort | Umesh Kumar Lilhore |
| collection | DOAJ |
| description | Abstract Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) and ant colony optimization (ACO) to tackle these challenges effectively. ACO is acknowledged for its proficiency in conducting local searches effectively, facilitating the swift discovery of high-quality solutions. In contrast, WWO specialises in global exploration, guaranteeing extensive coverage of the solution space. Collectively, these methods harness their distinct advantages to enhance various objectives: decreasing response times, maximising resource efficiency, and lowering operational expenses. We assessed the efficacy of our hybrid methodology by conducting extensive simulations using a cloud-sim simulator and a variety of workload trace files. We assessed our methods in comparison to well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey optimization (SMO), and ACO. Key performance indicators, such as task scheduling duration, execution costs, energy consumption, and resource utilisation, were meticulously assessed. The findings demonstrate that the hybrid WWO-ACO approach enhances task scheduling efficiency by 11%, decreases operational expenses by 8%, and lowers energy usage by 12% relative to conventional methods. In addition, the algorithm consistently achieved an impressive equilibrium in resource allocation, with balance values ranging from 0.87 to 0.95. The results emphasise the hybrid WWO-ACO algorithm’s substantial impact on improving system performance and customer satisfaction, thereby demonstrating a significant improvement in cloud computing optimisation techniques. |
| format | Article |
| id | doaj-art-aba1986cd0254f25b2a36da144c95d0c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aba1986cd0254f25b2a36da144c95d0c2025-08-20T02:11:46ZengNature PortfolioScientific Reports2045-23222025-04-0115112410.1038/s41598-025-96364-1A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniquesUmesh Kumar Lilhore0Sarita Simaiya1Yogendra Narayan Prajapati2Anjani Kumar Rai3Ehab Seif Ghith4Mehdi Tlija5Tarik Lamoudan6Abdelaziz A. Abdelhamid7School of Computing Science and Engineering, Galgotias UniversitySchool of Computing Science and Engineering, Galgotias UniversityDepartment of CSE, Ajay Kumar Garg Engineering CollegeDepartment of CEA, GLADepartment of Mechatronics, Faculty of Engineering, Ain Shams UniversityDepartment of Industrial Engineering, College of Engineering, King Saud UniversityDepartment of Mathematics, College of Science and Arts, Muhayil, King Khalid UniversityDepartment of Computer Science, College of Computing and Information Technology, Shaqra UniversityAbstract Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) and ant colony optimization (ACO) to tackle these challenges effectively. ACO is acknowledged for its proficiency in conducting local searches effectively, facilitating the swift discovery of high-quality solutions. In contrast, WWO specialises in global exploration, guaranteeing extensive coverage of the solution space. Collectively, these methods harness their distinct advantages to enhance various objectives: decreasing response times, maximising resource efficiency, and lowering operational expenses. We assessed the efficacy of our hybrid methodology by conducting extensive simulations using a cloud-sim simulator and a variety of workload trace files. We assessed our methods in comparison to well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey optimization (SMO), and ACO. Key performance indicators, such as task scheduling duration, execution costs, energy consumption, and resource utilisation, were meticulously assessed. The findings demonstrate that the hybrid WWO-ACO approach enhances task scheduling efficiency by 11%, decreases operational expenses by 8%, and lowers energy usage by 12% relative to conventional methods. In addition, the algorithm consistently achieved an impressive equilibrium in resource allocation, with balance values ranging from 0.87 to 0.95. The results emphasise the hybrid WWO-ACO algorithm’s substantial impact on improving system performance and customer satisfaction, thereby demonstrating a significant improvement in cloud computing optimisation techniques.https://doi.org/10.1038/s41598-025-96364-1Water wave optimizationHybrid optimizationAnt colony optimizationCloud load balancingResource allocation |
| spellingShingle | Umesh Kumar Lilhore Sarita Simaiya Yogendra Narayan Prajapati Anjani Kumar Rai Ehab Seif Ghith Mehdi Tlija Tarik Lamoudan Abdelaziz A. Abdelhamid A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques Scientific Reports Water wave optimization Hybrid optimization Ant colony optimization Cloud load balancing Resource allocation |
| title | A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques |
| title_full | A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques |
| title_fullStr | A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques |
| title_full_unstemmed | A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques |
| title_short | A multi-objective approach to load balancing in cloud environments integrating ACO and WWO techniques |
| title_sort | multi objective approach to load balancing in cloud environments integrating aco and wwo techniques |
| topic | Water wave optimization Hybrid optimization Ant colony optimization Cloud load balancing Resource allocation |
| url | https://doi.org/10.1038/s41598-025-96364-1 |
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