Intelligent and Reliable Routing for Audio/Video Mixed Traffic in Overlay Networks

Traditional route forwarding generates obvious performance problems and it cannot fulfill the increasing diversity in the number of user accesses and Quality of Service (QoS). It is necessary to investigating an intelligent and reliable routing for online audio/video mixed traffic with high-real time to meet different QoS requirements. In this article, we design a routing scheme based on deep reinforcement learning (DRL) and graph neural networks (GNNs), which could be easily implemented as an application on a controller, named RtDG. Specifically, a network topology is first extracted using GNN to generate high-dimensional feature representations. To obtain QoS utility values comprehensively, we set four parameters, namely, bandwidth, delay, packet loss rate, and delay jitter, and construct weighted formulas using the parameters determined by Bayesian optimization. We use proximal policy optimization (PPO) to make output decisions while adding a KL scatter penalty to the loss function. And then, the controller assigns it to switches via traffic table based on the calculated QoS values. Furthermore, we deploy an overlay network using Mininet and ONOS, enabling optimal pathfinding without changing the existing network architecture. Meanwhile, tests are conducted under background traffic of online audio/video. Extensive simulation results demonstrate that the RtDG can significantly reduce average delay and packet loss rate by 52.21% and 57.83%, compared to traditional routing strategies. Especially under high traffic conditions, it is able to consider the uncertainty during path selection and achieve excellent routing performance.