Enhancing Software Defined Networking Performance through Artificial Intelligence: A Comprehensive Analysis

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 decisionmaking 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.