Routing Optimization Algorithm under Deep Reinforcement Learning in Software Defined Network

Software defined networks, as an emerging network architecture, separate the control plane and data plane of the network, providing new ideas and methods for network management and optimization. In software defined networks, routing optimization is one of the key issues in improving network performance and flexibility. The current routing optimization algorithms often fail to fully consider the dynamics and complexity of the network, making it difficult to achieve intelligent management and optimization of the network. In order to accurately and timely perceive the dynamic changes of network traffic and improve network stability, this paper combines deep reinforcement learning to conduct in-depth research on routing optimization algorithms in software defined networks. This article first combines DQN (Deep Q Network) and Optimized Link State Routing (OLSR) protocols in deep reinforcement learning, capturing the relationships and traffic directions between different nodes in the network; Then, the link state information used for routing decisions is exchanged to solve the resource waste problem in Deep Deterministic Policy Gradient (DDPG), in order to achieve network optimization and intelligent management of network resources; Finally, based on real-time network status and service quality requirements, the optimal path is dynamically selected to respond to changes in the network environment and provide stable and reliable network services, thereby optimizing routing. The experimental results show that the average throughput of the DQN-OLSR route is 21.9% and 31.4% higher than that of the DQN and DDPG routes, respectively. The conclusion indicates that DQN-OLSR makes the network more flexible and helps to improve the service quality and management level of the entire network. Copyright © 2024 KSII.