Network load balancing method based on fuzzy clusterisation

Dmytro Kulchytskyi; Yevhen Zhukov

Journal: Volume 30, No. 3, 2025

Pages: 70 – 79

DOI: https://doi.org/10.62660/bcstu/3.2025.70

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Network load balancing method based on fuzzy clusterisation

Dmytro Kulchytskyi, Yevhen Zhukov

Received 30.04.2025

Revised 22.07.2025

Accepted 15.09.2025

Abstract

The rapid expansion of interactive video, augmented and virtual reality platforms, and the Internet of Things sharply increases the requirements for flexible traffic distribution in modern networks. Conventional static load balancing algorithms fail to respond to rapid shifts in traffic patterns, leading to overloads on individual nodes. The study aimed to design and experimentally validate an adaptive balancing algorithm based on fuzzy grouping of server states. Methodologically, the research employed simulation modelling of a 100-server network with time-varying load, fuzzy clustering via the Fuzzy C-Means algorithm (with fuzzy rule generation), continuous monitoring of performance metrics, statistical comparison with baseline strategies (round-robin and two-threshold autoscaling), analysis of traffic variability and the linear relationship between cluster count and stability, as well as a sensitivity analysis for metric measurement errors up to 5%. It was found that the proposed algorithm reduces the mean deviation of node utilisation by a factor of 1.5 compared with two-threshold autoscaling. During peak periods, system response time decreased from 180 to 145 ms, while average resource utilisation rose from 68% to 85%. Analysis of the traffic coefficient of variation showed that at values above 0.4 the new method keeps node load within ± 10% for 92% of observations, whereas round-robin exceeds this range in 37% of cases. A linear relation between the number of clusters and distribution stability was revealed, with four clusters proving optimal. Fuzzy rules additionally eliminate abrupt traffic oscillations after demand spikes and ensure smooth flow redirection. Sensitivity analysis indicates that a metric measurement error up to 5% does not affect decision correctness. The resulting architecture maintains stable operation when individual servers experience disproportionate traffic growth. Comparison with traditional approaches confirmed the superiority of the proposed method across all evaluated performance and robustness metrics

Keywords

computer networks; server load normalisation; variable traffic management; Fuzzy C-Means; adaptive algorithms; artificial intelligence; network nodes

References

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Alhilali, A.H., & Montazerolghaem, A. (2023). Artificial intelligence based load balancing in SDN: A comprehensive survey. ArXiv. doi: 10.48550/arXiv.2308.02149 .
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Chabira, C., Shayea, I., Nurzhaubayeva, G., Aldasheva, L., Yedilkhan, D., & Amanzholova, S. (2025). AI-driven handover management and load balancing optimization in ultra-dense 5G/6G cellular networks. Technologies, 13(7), article number 276. doi: 10.3390/technologies13070276 .
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Suggested citation

Kulchytskyi, D., & Zhukov, Ye. (2025). Network load balancing method based on fuzzy clusterisation. Bulletin of Cherkasy State Technological University, 30(3), 70-79. https://doi.org/10.62660/bcstu/3.2025.70

[1] Abadi, Z.J.K., & Mansouri, N. (2024). A comprehensive survey on scheduling algorithms using fuzzy systems in distributed environments. Artificial Intelligence Review, 57, article number 4. doi: 10.1007/s10462-023-10632-y .

[2] Alakeel, A.M. (2016). Application of fuzzy logic in load balancing of homogeneous distributed systems . International Journal of Computer Science and Security, 10(3), 95-106.

[3] Alhilali, A.H., & Montazerolghaem, A. (2023). Artificial intelligence based load balancing in SDN: A comprehensive survey. ArXiv. doi: 10.48550/arXiv.2308.02149 .

[4] Bedalli, E., Hajrulla, S., Rada, R., & Kosova, R. (2025). Fuzzy clustering approaches based on numerical optimizations of modified objective functions. Algorithms, 18(6), article number 327. doi: 10.3390/a18060327 .

[5] Chabira, C., Shayea, I., Nurzhaubayeva, G., Aldasheva, L., Yedilkhan, D., & Amanzholova, S. (2025). AI-driven handover management and load balancing optimization in ultra-dense 5G/6G cellular networks. Technologies, 13(7), article number 276. doi: 10.3390/technologies13070276 .

[6] Cheng, B., Li, D., & Zhu, X. (2024). Optimizing load scheduling and data distribution in heterogeneous cloud environments using a fuzzy-logic-based two-level framework. PLOS ONE, 19(12), article number e0310726. doi: 10.1371/journal.pone.0310726 .

[7] Gao, Y., Yang, H., Wang, X., Chen, Y., Li, C., & Zhang, X. (2022). A fuzzy-logic-based load balancing scheme for a satellite-terrestrial integrated network. Electronics, 11(17), article number 2752. doi: 10.3390/ electronics11172752 .

[8] Hu, P., Wang, S., Zhou, Y., & Zhang, J. (2024). Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage. Frontiers in Neuroinformatics, 18, article number 1440304. doi: 10.3389/fninf.2024.1440304 .

[9] Imanpour, S., Montazerolghaem, A., & Afshari, S. (2025). Optimizing server load distribution in multimedia IoT environments through LSTM-based predictive algorithms. ArXiv. doi: 10.48550/arXiv.2505.24806.

[10] Jin, X., Zhang, S., Ding, Y., & Wang, Z. (2024). Task offloading for multi-server edge computing in industrial internet with joint load balance and fuzzy security. Scientific Reports, 14, article number 27813. doi: 10.1038/ s41598-024-79464-2 .

[11] Karimi, A., Zarafshan, F., Jantan, A., Ramli, A.R., & Saripan, M.I. (2009). A new fuzzy approach for dynamic load balancing algorithm. International Journal of Computer Science and Information Security, 6(1). doi: 10.48550/ arXiv.0910.0317 .

[12] Khan, A.R. (2024). Dynamic load balancing in cloud computing: Optimized RL-based clustering with multi-objective optimized task scheduling. Processes, 12(3), article number 519. doi: 10.3390/pr12030519.

[13] Kreutz, D., Ramos, F.M.V., Verissimo, P.E., Rothenberg, C.E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14-76. doi: 10.1109/JPROC.2014.2371999 .

[14] Li, D.C.-H., Huang, C.-T., Tseng, C.-W., & Chou, L.-D. (2021). Fuzzy-based microservice resource management platform for edge computing in the Internet of Things. Sensors, 21(11), article number 3800. doi: 10.3390/s21113800 .

[15] Liu, B., Buyya, R., & Nadjaran Toosi, A. (2018). A fuzzy-based auto-scaler for web applications in cloud computing environments. In Service-oriented computing. ICSOC 2018. Lecture notes in computer science (pp. 797-811). Cham: Springer. doi: 10.1007/978-3-030-03596-9_57.

[16] Llorens-Carrodeguas, A., Leyva-Pupo, I., Cervelló-Pastor, C., Piñeiro, L., & Siddiqui, S. (2021). An SDN-based solution for horizontal auto-scaling and load balancing of transparent VNF clusters. Sensors, 21(24), article number 8283. doi: 10.3390/s21248283.

[17] Moura, B.M.P., Schneider, G.B., Yamin, A.C., & Santos, H.S. (2022). Interval-valued fuzzy logic approach for overloaded hosts in consolidation of virtual machines in cloud computing. Fuzzy Sets and Systems, 446, 144-166. doi: 10.1016/j.fss.2021.03.001 .

[18] Petrakis, E.G.M., Skevakis, V., Eliades, P., Aznavouridis, A., & Tsakos, K. (2024). ModSoftHP: Fuzzy microservices placement in Kubernetes. Electronics, 13(1), article number 65. doi: 10.3390/electronics13010065 .

[19] Qiu, H., Mao, W., Wang, C., Franke, H., Youssef, A., Kalbarczyk, Z.T., Başar, T., & Iyer, R.K. (2023). AWARE: Automate workload autoscaling with reinforcement learning in production cloud systems . In Proceedings of the 2023 USENIX annual technical conference (pp. 387-402). Boston: USENIX.

[20] Sarma, B., Kumar, R., & Tuithung, T. (2021). Optimised fuzzy clustering-based resource scheduling and dynamic load balancing algorithm for fog computing environment. International Journal of Computational Science and Engineering, 24(4), 343-353. doi: 10.1504/IJCSE.2021.117015 .

[21] Seraj, Y., Fadaei, S., Safaei, B., Javadi, A., Hosseini Monazzah, A.M., & Hemmatyar, A.M.A. (2024). LIMO: Load-balanced offloading with MAPE and particle swarm optimization in mobile fog networks. ArXiv. doi: 10.48550/arXiv.2408.14218.

[22] Silva, S.N., Goldbarg, M.A.S.d.S., da Silva, L.M.D., & Fernandes, M.A.C. (2024). Application of fuzzy logic for horizontal scaling in Kubernetes environments within the context of edge computing. Future Internet, 16(9), article number 316. doi: 10.3390/fi16090316 .

[23] Vashistha, D., Mehta, D., Kumhar, M., Bhatia, J., & Alkhayyat, A. (2025). Deep learning based load balancing in cloud computing: A survey. Procedia Computer Science, 259, 1963-1972. doi: 10.1016/j.procs.2025.04.152 .

[24] Wu, Y.-J., Chen, M.-C., Hwang, W.-S., & Cheng, M.-H. (2024). Dynamic routing using fuzzy logic for URLLC in 5G networks based on software-defined networking. Electronics, 13(18), article number 3694. doi: 10.3390/ electronics13183694 .

[25] Yi, B., Wang, X., Li, K., Das, S.K., & Huang, M. (2018). A comprehensive survey of network function virtualization. Computer Networks, 133, 212-262. doi: 10.1016/j.comnet.2018.01.021.

[26] Zhang, H., & Huang, S.-L. (2025). Improved fuzzy C-means clustering algorithm based on fuzzy particle swarm optimisation for solving data clustering problems. Mathematics and Computers in Simulation, 233, 311-329. doi: 10.1016/j.matcom.2025.02.012 .

[27] Zhu, S., Zhao, Y., & Yue, S. (2024). Double-constraint fuzzy clustering algorithm. Applied Sciences, 14(4), article number 1649. doi: 10.3390/app14041649.