Traffic Engineering in Hybrid Software Defined Network via Reinforcement Learning

Abstract

The emergence of Software Defined Network (SDN) provides a centralized and flexible approach to route network flows. Due to the technical and economic challenges in upgrading to a fully SDN-enabled network, hybrid SDN, with a partial deployment of SDN switches in a traditional network, has been a prevailing network architecture. Meanwhile, Traffic Engineering (TE) in the hydbrid SDN has attracted wide attentions from academia and industry. Previous studies on TE in the hybrid SDN are either traffic-oblivious or time-consuming, which causes routing schemes failed in responding to the dynamically-changing traffic rapidly and intelligently. Therefore, in this paper, we propose a Reinforcement Learning (RL) based method, which learns a traffic-splitting agent to address the dynamically-changing traffic and achieve the link load balancing in the hybrid SDN. Specifically, to rapidly and intelligently determine a routing scheme to the new traffic demands, a traffic-splitting agent is designed and learnt offline by exploiting the RL algorithm to establish the direct relationship between traffic demands and traffic-splitting policies. Once the traffic-splitting agent is learnt, the effective traffic-splitting policies, which are used to determine the traffic-splitting ratios on SDN switches, can be generated rapidly. Additionally, to meet the interactive requirements for learning a traffic-splitting agent, a reasonable simulation environment is proposed to be constructed to avoid routing loops when traffic-splitting policies are taken. Extensive evaluations on different topologies and real traffic demands demonstrate that the proposed method achieves the comparable network performance and performs superiorities in rapidly generating the satisfying routing schemes.