Enhancing SDN Performance: Machine Learning Integration with the POX Controller for Dynamic Routing and Congestion Management

Abstract

Efficient network management by SDN controllers is challenging in dynamic and high-traffic environments. Traditional controllers like POX I2_learning rely on static algorithms, adaptability, and limited scalability.  AI solutions are crucial to achieving optimal performance in complex networks. This work enhances the POX I2_learning controller to optimize its performance in dynamic and high-traffic networks, and then incorporates machine learning on the same platform. The improvements include real-time congestion metrics, adaptive timeouts, and load balancing, leading to improved scalability, stability, and congestion management. Also, an XG-Boost, a machine learning model, was incorporated to classify network states and improve routing decisions in real-time. The proposed method established above achieved a marked improvement in overall system performance and network control, including a stable latency of 3.52 ms, zero packet loss, and a slight improvement in throughput to 9.56 Mbps. The lightweight XG-Boost model with a compact size of 140 KB is delivered for optimal realization of real-time SDN application to offer an effective and dynamic network adaptation. This resulted in an overall accuracy of 99.67% with a balanced measure of precision, recall, and F1 score at 99%. These experimental results outperform recent SDN approaches in adaptability and performance and show that the system is reliable and able to predict a proactive decision, as well as, optimize resource usage and make the proposed framework relevant to SDN application developments.