An intelligent fuzzy reinforcement learning-based routing algorithm with guaranteed latency and bandwidth in SDN: Application of video conferencing services

Online video conferencing services connection by multipoint control units leads to communication bottleneck and high latency. Due to the dynamic traffic in routing connection requests to video conferences, the existing routing algorithms face problems such as communication bottlenecks, high latency, congestion, path redundancy, and local optimum. In recent years, the implementation of video conferencing systems using Software-Defined Networking (SDN) provides new opportunities to improve Quality of Service (QoS) with low latency. In this paper, an I ntelligent F uzzy reinforcement learning-based R outing A lgorithm is proposed, which simultaneously G uarantees L atency and B andwidth for online video conferencing services in SDN (named IFRA-GLB). Fuzzy logic mainly performs online routing between an ingress–egress pair. Meanwhile, through ongoing training, reinforcement learning lowers the average number of hops on the path determined by fuzzy model. Enhancements to the convergence capability of IFRA and reduced reliance on network topology are achieved by adjusting link weights using a weighted shortest path algorithm focused on critical nodes. When the network encounters congestion, a deferral module is applied to assign higher priority to requests with lower resource demands. Experimental results demonstrate that IFRA-GLB significantly improves performance and convergence compared to existing solutions, enhancing the QoS for video conferencing services. Specifically, IFRA-GLB increases the average admission rate by 1.96% for MIRA topology and 2.71% for ANSNET topology with 3.5% lower latency.