doi: 10.18178/jacn.2025.13.2.296
Software Defined Networks with Artificial Intelligence: A Comprehensive Analysis and Review
Qutaiba I. Ali
*
,
Ola Marwan Assim
,
Zahraa Talal
,
Nawal Younis
- Department of Computer, College of Engineering, University of Mosul, Iraq
- Manuscript received June 10, 2025
- accepted July 24, 2025
- published November 7, 2025
Abstract
Software-Defined Networking (SDN) introduces a paradigm shift in network management by decoupling the control and data planes, thereby enabling centralized, programmable network control. However, the dynamic and complex nature of modern traffic demands adaptive and intelligent decision-making beyond traditional rule-based systems. This paper explores the integration of Artificial Intelligence (AI) techniques—particularly supervised learning algorithms—into the SDN control architecture to improve performance, efficiency, and automation. The study provides an overview of SDN architecture and the OpenFlow protocol, followed by an empirical evaluation using real traffic scenarios. Multiple AI models including Support Vector Machine (SVM), Naïve Bayes (NB), and Nearest Centroid were tested on a software-defined testbed. Performance metrics such as classification accuracy, throughput, latency, packet loss, and controller decision time were analyzed. Results demonstrate that AI integration leads to significant improvements across all metrics, validating the potential of AI-SDN synergy in creating intelligent and self-optimizing networks.
Keywords
- Software-Defined Networking (SDN)
- Artificial Intelligence (AI)
- Machine Learning (ML)
- OpenFlow protocol
- Ryu controller
How to Cite
Qutaiba I. Ali, Ola Marwan Assim, Zahraa Talal, and Nawal Younis, "Software Defined Networks with Artificial Intelligence: A Comprehensive Analysis and Review," Journal of Advances in Computer Networks, vol. 13, no. 2, pp. 31-36, 2025. https://doi.org/ 10.18178/jacn.2025.13.2.296
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).