Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming from traditional to intelligent networking. The increasing demand for services cloud users has increased the load on network. An efficient system must handle various loads and needs representing relationships dependence of businesses automated measurement systems guarantee quality service (QoS). multiple paths source destination give scope select an optimal path by maintaining equilibrium using some best algorithms. Moreover, requests need be transferred reliable network elements. To address SDN’s current future challenges, there is know how artificial intelligence (AI) optimization techniques can efficiently balance load. This study aims explore two techniques, namely Ant Colony Optimization (ACO) Particle Swarm (PSO), used balancing SDN. Further, we identified that modification existing technique could improve performance link node form reach target balancing. Finally, propose conceptual framework SDN futurology evaluating reliability, which QoS