Scalable Hybrid Switching-Inspired Sofware Defined Networking Challenges: From the Perspective of Machine Learning Approach

The Software-Defined Networking technology promises to enhance network performance and reduce costs for service providers by providing scalability, flexibility, and programmability through the separation of the control plane from the data plane. However, the separation between the control plane and data plane in the implementation of SDN presents scalability issues, as the controller has limited computational resources. In this document, we propose an SDN hybrid model switch as a scalable framework for hybrid routing with machine learning. The proposed hybrid switching solution is benchmarked against an OpenFlow switch based on network performance metrics, such as throughput, packet exchange rate, delay, and CPU load. The simulation results are statistically examined and compared by plotting the performance results on a graph. The proposed hybrid switching solution can be implemented into a Data Centre network, to guarantee scalable switching and enhanced network latency.