Optimised adaptive load balancing method in SDN networks using the adaptive ANT colony approach

dc.citation.epage66
dc.citation.issue4
dc.citation.journalTitleВимірювальна техніка та метрологія
dc.citation.spage62
dc.contributor.affiliationNTUU “Igor Sikorsky Kyiv Polytechnic Institute”
dc.contributor.affiliationKing Saud University
dc.contributor.authorShchur, Vadym
dc.contributor.authorAlZubi, Ahmad Ali
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-04-03T07:16:38Z
dc.date.available2024-04-03T07:16:38Z
dc.date.created2023-03-01
dc.date.issued2023-03-01
dc.description.abstractIn modern software-defined networks, providing efficient load balancing is a crucial task for optimal resource utilization and ensuring stable quality of service. To achieve these goals, in this paper, we propose a new innovative loadbalancing method for SDN networks based on an anticolonial approach with dynamic parameter settings. This proposed method demonstrates high efficiency in the face of variable network dynamics and diverse node loads. Its main advantage is the ability to adapt to changing load and traffic conditions in real-time. The algorithm continuously analyses the load on the nodes and dynamically adjusts the weighting factors to ensure optimal traffic distribution. The proposed method stands out due to its ability to effectively maintain load balance under a variety of calls and loads, making it a powerful tool for ensuring reliability and performance in networks.
dc.format.extent62-66
dc.format.pages5
dc.identifier.citationShchur V. Optimised adaptive load balancing method in SDN networks using the adaptive ANT colony approach / Vadym Shchur, Ahmad Ali AlZubi // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 4. — P. 62–66.
dc.identifier.citationenShchur V. Optimised adaptive load balancing method in SDN networks using the adaptive ANT colony approach / Vadym Shchur, Ahmad Ali AlZubi // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 4. — P. 62–66.
dc.identifier.doidoi.org/10.23939/istcmtm2023.04.062
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61621
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВимірювальна техніка та метрологія, 4 (84), 2023
dc.relation.ispartofMeasuring Equipment and Metrology, 4 (84), 2023
dc.relation.references[1] A. Garcia-Saavedra, P. Serrano, A. Banchs, X. CostaPerez, "Machine learning for network automation: Overview, architecture, and applications," IEEE Communications Magazine, vol. 56, no. 3, pp. 11–17, 2018. DOI: 10.1109/MCOM.2018.1700980.
dc.relation.references[2] L. Tang, S. Han, H. Zhang, and M. Gerla, "Resilient SDN traffic engineering: A survey," IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2682–2706, 2016. DOI: 10.1109/COMST.2016.2581599.
dc.relation.references[3] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29-41, 1996. DOI: 10.1109/3477.484436.
dc.relation.references[4] M.saied, S. Saha, I. Sayem. (2022). A Comparative Study on Load Balancing Techniques in Software Defined Networks, January 2022, DOI:10.35444/IJANA.2022.13401
dc.relation.references[5] Ghorbani, S., Sama, M. R. B., & Abolhasani, M. (2015). An efficient load balancing algorithm for software-defined networking. In Proceedings of the 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (pp. 80-85). https://www.researchgate.net/publication/329144198_Efficient_load_balancing_algorithm_in_cloud_computing.
dc.relation.references[6] Chen, C. C., Chen, M. (2015). A weighted round-robin algorithm for software-defined networking. In Proceedings of the 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (pp. 138–143). https://www.springerprofessional.de/en/weighted-roundrobin-load-balancing-algorithm-for-softwaredefin/16887408
dc.relation.references[7] Bari, M. F., Boutaba, R., Esteves, R., Granville, L. Z., Podlesny, M., Rabbani, G., & Zhang, Q. (2013). Data center network virtualization: A survey. IEEE Communications Surveys & Tutorials, 15(3), 1614–1634 https://www.academia.edu/26829535/Data_Center_Network_Virtualization_A_Survey
dc.relation.references[8] Y. Tao et al, A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control, Appl. Sci. 2021, 11(8), 3605; https://doi.org/10.3390/app11083605
dc.relation.references[9] R. Mehmood, F. Ahmed, "Enhanced dynamic ant colony load balancing algorithm for SDN," Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 5907–5921, 2020. DOI: 10.1007/s12652-020-01906-4.
dc.relation.referencesen[1] A. Garcia-Saavedra, P. Serrano, A. Banchs, X. CostaPerez, "Machine learning for network automation: Overview, architecture, and applications," IEEE Communications Magazine, vol. 56, no. 3, pp. 11–17, 2018. DOI: 10.1109/MCOM.2018.1700980.
dc.relation.referencesen[2] L. Tang, S. Han, H. Zhang, and M. Gerla, "Resilient SDN traffic engineering: A survey," IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2682–2706, 2016. DOI: 10.1109/COMST.2016.2581599.
dc.relation.referencesen[3] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29-41, 1996. DOI: 10.1109/3477.484436.
dc.relation.referencesen[4] M.saied, S. Saha, I. Sayem. (2022). A Comparative Study on Load Balancing Techniques in Software Defined Networks, January 2022, DOI:10.35444/IJANA.2022.13401
dc.relation.referencesen[5] Ghorbani, S., Sama, M. R. B., & Abolhasani, M. (2015). An efficient load balancing algorithm for software-defined networking. In Proceedings of the 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (pp. 80-85). https://www.researchgate.net/publication/329144198_Efficient_load_balancing_algorithm_in_cloud_computing.
dc.relation.referencesen[6] Chen, C. C., Chen, M. (2015). A weighted round-robin algorithm for software-defined networking. In Proceedings of the 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (pp. 138–143). https://www.springerprofessional.de/en/weighted-roundrobin-load-balancing-algorithm-for-softwaredefin/16887408
dc.relation.referencesen[7] Bari, M. F., Boutaba, R., Esteves, R., Granville, L. Z., Podlesny, M., Rabbani, G., & Zhang, Q. (2013). Data center network virtualization: A survey. IEEE Communications Surveys & Tutorials, 15(3), 1614–1634 https://www.academia.edu/26829535/Data_Center_Network_Virtualization_A_Survey
dc.relation.referencesen[8] Y. Tao et al, A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control, Appl. Sci. 2021, 11(8), 3605; https://doi.org/10.3390/app11083605
dc.relation.referencesen[9] R. Mehmood, F. Ahmed, "Enhanced dynamic ant colony load balancing algorithm for SDN," Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp. 5907–5921, 2020. DOI: 10.1007/s12652-020-01906-4.
dc.relation.urihttps://www.researchgate.net/publication/329144198_Efficient_load_balancing_algorithm_in_cloud_computing
dc.relation.urihttps://www.springerprofessional.de/en/weighted-roundrobin-load-balancing-algorithm-for-softwaredefin/16887408
dc.relation.urihttps://www.academia.edu/26829535/Data_Center_Network_Virtualization_A_Survey
dc.relation.urihttps://doi.org/10.3390/app11083605
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectsoftware-defined networking
dc.subjectload balancing
dc.subjectSDN networks
dc.subjectadaptive anticolonial method
dc.subjectdynamic parameter adjustment
dc.titleOptimised adaptive load balancing method in SDN networks using the adaptive ANT colony approach
dc.typeArticle

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