Deep Reinforcement Learning-Based Routing on Software-Defined Networks

With an exponential increase in network traffic demands requiring quality of services, the need for routing optimization has become more prominent. Recently, the advent of software-defined networking (SDN) technology enables centralized management and operation, and the networking resources such as switches become flexibly configurable through programmable interfaces. In this paper, we propose a deep reinforcement learning (DRL)-based routing optimization on an SDN. In the proposed method, the DRL agent learns the interdependency between the traffic load of network switches and the network performance, and decides an optimal set of link weights to make a balance between the end-to-end delay and packet losses of the network. The SDN controller determines the routing paths using the set of link weights and installs the flow-rules on the SDN-enabled switches. To overcome an extensively long learning process of DRL in a case of topology change, we develop an M/M/1/K queue-based network model and perform the learning process of DRL using the network model in an offline manner until it is converged. The simulation results demonstrate the proposed routing method outperforms a conventional hop-count routing and a traffic demand-based RL algorithm in several network topologies.