Traffic engineering based on deep reinforcement learning in hybrid IP/SR network

Abstract

Segment Routing (SR) is a new routing paradigm based on source routing and provide traffic engineering (TE) capabilities in IP network. By extending interior gateway protocol(IGP), SR can be easily applied to IP network. However, upgrading current IP network to a full SR one can be costly and difficult. Hybrid IP/SR network will last for some time. Aiming at the low flexibility problem of static TE policies in the current SR networks, this paper proposes a Deep Reinforcement Learning (DRL) based TE scheme. The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution. As a result, the network congestion can be mitigated and the performance of the network is improved. Simulation results show that our proposed scheme can improve the throughput of the network by up to 9% than existing schemes.