A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks

Abstract

Next-gen networks require significant evolution of management to enable automation and adaptively adjust network configuration based on traffic dynamics. The advent of software-defined networking (SDN) and programmable switches enables flexibility and programmability. However, traditional techniques that decide traffic policies are usually based on hand-crafted programming optimization and heuristic algorithms. These techniques make non-realistic assumptions, e.g., considering static network load and topology, to obtain tractable solutions, which are inadequate for next-gen networks. In this paper, we design and develop a deep reinforcement learning (DRL) approach for adaptive traffic routing. We design a deep graph convolutional neural network (DGCNN) integrated into the DRL framework to learn the traffic behavior from not only the network topology but also link and node attributes. We adopt the Deep Q-Learning technique to train the DGCNN model in the DRL framework without the need for a labeled training dataset, enabling the framework to quickly adapt to traffic dynamics. The model leverages q-value estimates to select the routing path for every traffic flow request, balancing exploration and exploitation. We perform extensive experiments with various traffic patterns and compare the performance of the proposed approach with the Open Shortest Path First (OSPF) protocol. The experimental results show the effectiveness and adaptiveness of the proposed framework by increasing the network throughput by up to 7.8% and reducing the traffic delay by up to 16.1% compared to OSPF.