A Hybrid Graph Neural Network based Adaptive Routing Optimization in Software-Defined Networks

Abstract

Adaptive routing techniques that go beyond the constraints of conventional algorithms are required due to the extraordinary increase in network traffic. The advancements in deep reinforcement learning (DRL) techniques enforce the dynamic learning of the paths in network. This paper presents a hybrid DRL based Graph Neural Network combined with Soft Actor Critic(GNN-SAC) method in a dynamic Software Defined Network(SDN) environment using M/M/1 queuing model. Its performance is compared with other four DRL techniques-Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC). The experimental results demonstrate that the hybrid GNN-SAC achieves the best path cost of 4, highest throughput of 25.18 Mbps, and lowest packet loss of 6.02\%, whereas SAC achieves the lowest delay of 14.74 ms and jitter of 1.16 ms, however, A2C achieves the fastest training time of $0.23 s$ and best link utilization of 45.58\%. This platform also provides real-time animated packet routing visualization with live failure simulation capabilities.