IQoR-LSE: An Intelligent QoS On-Demand Routing Algorithm With Link State Estimation
Bin Dai, Yuanyuan Cao, Zhongli Wu, Yang Xu
Abstract
With the rapid development of Internet applications, diversified Quality of Service (QoS) has been required in packet routing to meet the demand of various types of applications. In this article, an Intelligent QoS on-demand Routing (IQoR) framework has been proposed to support multiclass QoS Provisioning for packet forwarding. In addition, we present an IQoR with Link State Estimation (IQoR-LSE) algorithm with the assistance of link congestion inference to guide the exploration of action space in deep reinforcement learning, to seek optimal routing policies. The IQoR-LSE algorithm is proposed to solve the non-convergence problem at exploring the high-dimensional action space by jointly estimating the link congestion. Extensive simulations show that IQoR-LSE algorithm outperforms other benchmark routing algorithms with efficient learning and a significant reduction in average delay, jitter and packet loss.