Intelligent traffic engineering methods in SDN: Performance analysis and prospects for application

Authors

DOI:

https://doi.org/10.18372/2073-4751.84.20897

Keywords:

artificial intelligence, enterprise networks, SDN, Smart Grids, transport networks, UAV networks

Abstract

Software-defined networks (SDN) create favorable conditions for deploying intelligent traffic management methods thanks to centralized control and global visibility of the network state. At the same time, traditional load balancing algorithms cannot effectively adapt to dynamic traffic changes and complex topologies. This paper analyzes the effectiveness of the intelligent load-balancing method LBBNN, based on an artificial neural network that considers the global network state when selecting a route. The evaluation was carried out in a Mininet environment on sparsely connected, fully connected, and de Bruijn topologies. The results demonstrate reduced delays and packet loss and a more uniform load distribution compared to Round Robin, Dynamic Load Balancing (DLB), and modified ECMP. The neural-network approach improves quality of service (QoS) and enables more efficient use of network resources in dynamic SDN environments.

References

Bera, S., Misra, S., Vasilakos, A.V.: ‘Software-defined networking for internet of things: a survey’, IEEE Internet Things J., 2017, 4, (6), pp. 1994–2008

Lin, P., Bi, J., Wolff, S. et al: ‘A west-east bridge based SDN inter-domain testbed’, IEEE Commun. Mag., 2015, 53, (2), pp. 190–197

Jain, S., Kumar, A., Mandal, S. et al: ‘B4: experience with a globally-deployed software defined WAN’, ACM SIGCOMM Comput. Commun. Rev., 2013, 43, (4), pp. 3–14

Kulakov, Y. O., and D. V. Korenko. "Methods of applying artificial intelligence in software-defined networks." Problems of Informatization and Management 1.73 (2023): pp. 23-27.

Loutskii, H., Volokyta, A., Rehida, P., Kaplunov, A., Ivanishchev, B., Honcharenko, O., & Korenko, D. (2021). Topology synthesis method based on excess de bruijn and dragonfly. In Advances in Computer Science for Engineering and Education IV (pp. 315-325). Springer International Publishing.

Volokyta A., Loutskii H., Rehida P., Honcahrenko O., Korenko D., Rusinov V., Ivanishchev B., Kaplunov A. Convolutionary neural networks regarding problem of monitoring data balancing in de bruijn topology. Bulgarian Journal for Engineering Design, 2021, Mechanical Engineering Faculty, Technical University-Sofia. ISSN 1313-7530

Volokyta, A., Loutskii, H., Rehida, P., Kaplunov, A., Ivanishchev, B., Honcharenko, O., & Korenko, D. (2022). Extended DragonDeBrujin topology synthesis method. International Journal of Computer Network and Information Security, 9(6), 23.

Jain, R., Paul, S.: ‘Network virtualization and software defined networking for cloud computing: a survey’, IEEE Commun. Mag., 2013, 51, (11), pp. 24–31

Artem Volokyta, Alla Kogan, Oleksii Cherevatenko, Dmytro Korenko, Dmytro Oboznyi, Yurii Kulakov, "Traffic Engineering with Specified Quality of Service Parameters in Software-defined Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.16, No.5, pp.1-13, 2024. DOI:10.5815/ijcnis.2024.05.01

Published

2025-12-30

How to Cite

Korenko, D., Bulakh, L., & Kulakov, Y. (2025). Intelligent traffic engineering methods in SDN: Performance analysis and prospects for application. Problems of Informatization and Management, 4(84), 66–74. https://doi.org/10.18372/2073-4751.84.20897

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