EARS: Intelligence-driven experiential network architecture for automatic routing in software-defined networking
Yuxiang Hu, Ziyong Li, Julong Lan, Jiangxing Wu, Lan Yao
Abstract
Software-Defined Networking (SDN) adapts logically-centralized control by decoupling control plane from data plane and provides the efficient use of network resources. However, due to the limitation of traditional routing strategies relying on manual configuration, SDN may suffer from link congestion and inefficient bandwidth allocation among flows, which could degrade network performance significantly. In this paper, we propose EARS, an intelligence-driven experiential network architecture for automatic routing. EARS adapts deep reinforcement learning (DRL) to simulate the human methods of learning experiential knowledge, employs the closed-loop network control mechanism incorporating with network monitoring technologies to realize the interaction with network environment. The proposed EARS can learn to make better control decision from its own experience by interacting with network environment and optimize the network intelligently by adjusting services and resources offered based on network requirements and environmental conditions. Under the network architecture, we design the network utility function with throughput and delay awareness, differentiate flows based on their size characteristics, and design a DDPG-based automatic routing algorithm as DRL decision brain to find the near-optimal paths for mice and elephant flows. To validate the network architecture, we implement it on a real network environment. Extensive simulation results show that EARS significantly improve the network throughput and reduces the average packet delay in comparison with baseline schemes (e.g. OSPF, ECMP).