Load Balancing model based on Machine Learning and Segment Routing in SDN

Because of increased number of network devices in the world, as well as the need for the network environment to provide dynamics of faults and adaptability to load changes, it is difficult and complicated network to be managed. By applying an approach called Software Defined Networks (SDN), the separation of the control and data planes is achieved. This allows the creation and deployment of new network applications to be easier, as well as provides simplification and flexibility in network policy enforcement, facilitating network configuration and management. The main problems in SDN are load balancing and segment routing. This paper proposes a model which aims to reduce not only the overall load on the network, but also to reduce the bandwidth and improve the routing mechanism on the SDN networks. It combines segment routing algorithm and load balancing mechanisms based on machine learning.