The application and performance optimization of multi-controller-based load balancing algorithm in computer networks
This paper addresses the critical issue of network congestion caused by the increase in network traffic in contemporary society. The computer networks serve as the foundation for information exchange and online services, and their efficiency is essential. Traditional load-balancing algorithms face c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000714 |
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| _version_ | 1850201683361005568 |
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| author | Fengfeng Guo Ailing Ye |
| author_facet | Fengfeng Guo Ailing Ye |
| author_sort | Fengfeng Guo |
| collection | DOAJ |
| description | This paper addresses the critical issue of network congestion caused by the increase in network traffic in contemporary society. The computer networks serve as the foundation for information exchange and online services, and their efficiency is essential. Traditional load-balancing algorithms face challenges in handling dynamic workloads, leading to inefficient resource utilization and extended response time. To address this problem, a novel method called Genetic-Bird Swarm Optimization (GBSO) is introduced, focusing on multi-controller-based load balancing. This method involves problem modeling, analysis, and selection processes, including the selection of switches and target controllers within the network segment. The results showed that the throughput of the proposed GBSO method was about 3800, and the load index after load balancing was 0.6, indicating that the workload distribution was balanced. The accuracy of the proposed GBSO algorithm was 92.15 %, the precision was 89 %, the recall rate was 88 %, and the F1 score was 85 %, all of which were higher than the existing Naive Bayes algorithm. This study emphasizes the importance of load balancing in optimizing computer network performance. The new algorithm proposed in this article provides a reliable solution for uniform network traffic distribution, reducing the limitations of existing methods. |
| format | Article |
| id | doaj-art-e09d13c67c324b8ba6cfd3167d436c3b |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-e09d13c67c324b8ba6cfd3167d436c3b2025-08-20T02:11:57ZengElsevierEgyptian Informatics Journal1110-86652025-06-013010067810.1016/j.eij.2025.100678The application and performance optimization of multi-controller-based load balancing algorithm in computer networksFengfeng Guo0Ailing Ye1Computer Information Department of Suzhou Vocational and Technical College, Suzhou 234000, China; Corresponding author.Network Engineering College & Wuhu Institute of Technology, Wuhu 241003, China; Faculty of Information Science & Technology Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaThis paper addresses the critical issue of network congestion caused by the increase in network traffic in contemporary society. The computer networks serve as the foundation for information exchange and online services, and their efficiency is essential. Traditional load-balancing algorithms face challenges in handling dynamic workloads, leading to inefficient resource utilization and extended response time. To address this problem, a novel method called Genetic-Bird Swarm Optimization (GBSO) is introduced, focusing on multi-controller-based load balancing. This method involves problem modeling, analysis, and selection processes, including the selection of switches and target controllers within the network segment. The results showed that the throughput of the proposed GBSO method was about 3800, and the load index after load balancing was 0.6, indicating that the workload distribution was balanced. The accuracy of the proposed GBSO algorithm was 92.15 %, the precision was 89 %, the recall rate was 88 %, and the F1 score was 85 %, all of which were higher than the existing Naive Bayes algorithm. This study emphasizes the importance of load balancing in optimizing computer network performance. The new algorithm proposed in this article provides a reliable solution for uniform network traffic distribution, reducing the limitations of existing methods.http://www.sciencedirect.com/science/article/pii/S1110866525000714Load balancingMultiple controllersGenetic-bird swarm optimizationComputer networks |
| spellingShingle | Fengfeng Guo Ailing Ye The application and performance optimization of multi-controller-based load balancing algorithm in computer networks Egyptian Informatics Journal Load balancing Multiple controllers Genetic-bird swarm optimization Computer networks |
| title | The application and performance optimization of multi-controller-based load balancing algorithm in computer networks |
| title_full | The application and performance optimization of multi-controller-based load balancing algorithm in computer networks |
| title_fullStr | The application and performance optimization of multi-controller-based load balancing algorithm in computer networks |
| title_full_unstemmed | The application and performance optimization of multi-controller-based load balancing algorithm in computer networks |
| title_short | The application and performance optimization of multi-controller-based load balancing algorithm in computer networks |
| title_sort | application and performance optimization of multi controller based load balancing algorithm in computer networks |
| topic | Load balancing Multiple controllers Genetic-bird swarm optimization Computer networks |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525000714 |
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