Intelligent traffic management method in software-defined networks based on behavioral classification and adaptive priority service
DOI:
https://doi.org/10.20535/2786-8729.7.2025.334049Keywords:
software-defined networks, intelligent traffic management, behavioral classification, adaptive priority service, deep packet inspection, class-based queuing, weighted random early detection, quality of serviceAbstract
The growing complexity of modern enterprise network environments demands sophisticated traffic management solutions that can provide quality of service (QoS) guarantees for encrypted and heterogeneous flows. Existing traffic management approaches face significant challenges when dealing with encrypted protocols and diverse application requirements, resulting in performance degradation for critical services and inefficient resource utilization. This paper addresses the problem of intelligent traffic management in software-defined networks through behavioral classification and adaptive priority service mechanisms.
The study examines the development and implementation of an integrated traffic management method that combines behavioral deep packet inspection, class-based queuing, and weighted random early detection algorithms. The research investigates how behavioral flow characteristics remain observable in encrypted traffic environments and how these patterns can be leveraged for effective QoS provisioning. The proposed method utilizes packet timing patterns, connection behaviors, and flow statistics to classify traffic without relying on payload inspection or predefined port assignments.
Experimental validation through discrete-event simulation demonstrates significant performance improvements compared to traditional first-in-first-out mechanisms. The behavioral classification component achieves over 95% classification accuracy. The experimental results demonstrate up to 97.5% improvement in latency performance and 0% packet loss for high-priority traffic.
Integrating behavioral traffic recognition with adaptive queue management within a programmable network framework provides an effective and innovative approach to maintaining stable service quality in encrypted, multi-service environments. The proposed method is compatible with existing software-defined network controllers and can be deployed without modification of application protocols or infrastructure components.
References
A. M. R. Ruelas, J. Q. Ccorimanya, and M. A. Q. Barra, “An Overview of P4-Based Load Balancing Mechanism in SDN,” in Smart Innovation, Systems and Technologies, vol. 353 SIST, Springer Science and Business Media Deutschland GmbH, 2023, pp. 174–179. https://doi.org/10.1007/978-3-031-31007-2_17.
E. Hajian, M. R. Khayyambashi, and N. Movahhedinia, “A Mechanism for Load Balancing Routing and Virtualization Based on SDWSN for IoT Applications,” IEEE Access, vol. 10, pp. 37457–37476, 2022, https://doi.org/10.1109/ACCESS.2022.3164693.
N. Lo and I. Niang, “SDN-based QoS architectures in Edge-IoT Systems: A Comprehensive Analysis,” in 2023 IEEE World AI IoT Congress, AIIoT 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 605–611. https://doi.org/10.1109/AIIoT58121.2023.10174349.
P. Podili and K. Kataoka, “TRAQR: Trust aware End-to-End QoS routing in multi-domain SDN using Blockchain,” Journal of Network and Computer Applications, vol. 182, May 2021, https://doi.org/10.1016/j.jnca.2021.103055.
M. S. Raza, S. B. A. Kazmi, R. Ali, M. M. Naqvi, H. Fiaz, and A. Akram, “High Performance DPI Engine Design for Network Traffic Classification, Metadata Extraction and Data Visualization,” in 2024 5th International Conference on Advancements in Computational Sciences, ICACS 2024, Institute of Electrical and Electronics Engineers Inc., 2024. https://doi.org/10.1109/ICACS60934.2024.10473274.
Y. Su, P. Jiang, H. Chen, and X. Deng, “A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid,” Applied Sciences (Switzerland), vol. 12, no. 15, Aug. 2022, https://doi.org/10.3390/app12157629.
S. K. Keshari, V. Kansal, and S. Kumar, “A Systematic Review of Quality of Services (QoS) in Software Defined Networking (SDN),” Wirel Pers Commun, vol. 116, no. 3, pp. 2593–2614, Feb. 2021, https://doi.org/10.1007/s11277-020-07812-2.
W. K. Chiang and T. Y. Li, “An Extended SDN Architecture for Video-on-Demand Caching,” Mobile Networks and Applications, 2024, doi: https://doi.org/10.1007/s11036-024-02321-z.
D. Oboznyi and Y. Kulakov, “Algorithm for orchestration of encrypted traffic in SDN networks,” Problems of Informatization and Control, vol. 1, no. 81, pp. 52–58, Jun. 2025, https://doi.org/10.18372/2073-4751.81.20129.
L. Kleinrock, Theory, Volume 1, Queueing Systems. USA: Wiley-Interscience, 1975. [Online]. Available: https://ia601403.us.archive.org/13/items/in.ernet.dli.2015.134547/2015.134547.Queueing-Systems-Volume-1-Theory.pdf. Accessed: Apr. 12, 2025.
