Robust SDN Synchronization in Mobile Networks using Deep Reinforcement and Transfer Learning

A logically centralized controller architecture for SDN deployments is a well understood and implemented method, however because of issues such as scalability, privacy and more, there is a need to develop and implement a robust physically distributed SDN controller architecture. In a distributed SDN environment, a centralized logical network view needs to be maintained, which means the distributed controllers need a robust method of remaining informed about other controller's network through synchronization. This is specially true in mobile, wireless networks with changing controller and network environment, so to this end we develop a deep reinforcement and transfer learning based method that provides the controllers with an efficient policy for synchronizing with other controllers and maintaining a logically centralized view in such networks. We show that our application-centric method performs well for different kinds of applications including shortest path routing and load balancing, outperforming a reinforcement learning based method as well as a round robin method.