Enhanced Performance of Software Defined Network (SDN) Through Meta Heuristic Particle Swarm Optimization Random Forest Algorithm

Integration of Software-defined Networking (SDN) with traditional networking technologies is crucial due to the increasing demand for network performance. SDN offers a centralized and programmable solution for managing the network, while legacy networking systems provide stability and compatibility with existing infrastructures. However, integrating these two systems often results in suboptimal network performance. The Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm is suggested in this research as a potential solution to this problem of network performance improvement in SDN-legacy network integration. PSO and RF are metaheuristic algorithms that have been successfully applied to many optimization problems, including network optimization. The proposed solution used PSO to optimize the routing of packets in the SDN-legacy network, resulting in improved network performance and reduced latency. Simulation outcomes demonstrate the success of the proposed solution and show that it outperforms existing solutions in terms of network performance. When the outcomes are contrasted with other field-wide algorithms already in use, it is found that PSO and RF perform better than the competitors. The results show that PSO-RF decreases end-to-end delay and enhance the performance from 99% to 100% compare to other meta-heuristic algorithms.