Deep Reinforcement Learning for Controller Placement in Software Defined Network

Abstract

Controller placement is a critical problem in Software Defined Network (SDN), which has been identified as a potential approach to achieve a more flexible control and management of the network. To achieve an optimal placement solution, the network characters as well as flow fluctuations should be fully considered, making the problem extraordinary complicated. Deep Reinforcement Learning (DRL) has vast potential to obtain suitable results by exploring the solution space, and be adapted to the rapidly fluctuating data flow with the algorithm learning from the feedback generated during exploration. In this paper, we propose a Deep Q-Network (DQN) empowered Dynamic flow Data Driven approach for Controller Placement Problem (D4CPP). D4CPP integrates the historical network data learning into the controller deployment and realtime switch-controller mapping decision, so as to be adapted to the dynamic network environment with flow fluctuations. Specifically, D4CPP takes the flow fluctuation, data latency, and load balance into full consideration, and can reach an optimized balance among these metrics. Extensive simulations show that D4CPP is efficient in SDN system with dynamic flow fluctuating, and outperforms traditional scheme by 13% in latency and 50% in load balance averagely when the latency and the load balance are assigned with the same weight.