Dynamic Path Selection in SDN Based on Reinforcement Learning and Link Utilization

INTRODUCTION: The development of modern power systems imposes stringent requirements on communication networks, including highly dynamic loads, low latency, and high reliability. Although recent Software-Defined Networking routing, link-utilization-aware scheduling, and reinforcement learning-based methods improve path optimization, challenges remain in bottleneck-link perception, congestion feedback, and stable decision-making under dynamic traffic conditions.

Objectives

To counteract problems like delayed response times and inadequate congestion identification in conventional path selection algorithms owing to dynamic link modifications, this research paper presents a path selection model that combines bottleneck link usage and reinforcement learning.

Methods

Under the Software-Defined Networking control architecture, the proposed model incorporates link utilization, Graph Convolutional Network structures, and Gated Recurrent Units, while introducing a deep reinforcement learning algorithm to optimize routing strategies.

Results

Experimental results demonstrate that under a 60 Mbit/s load, the proposed method achieves a throughput of 57 Mbit/s, maintains the minimum transmission delay within 0.043 s, and yields a link utilization rate of 89%. In experiments carried out to evaluate dynamic decision-making, the approach for choosing paths adopted by the model averages convergence at the 15th round, resulting in an error rate of 4.2%, minimal load balancing at 0.23, and median latency time in real-time decisions of only 31 ms, which is better than other models.

Conclusion

These results demonstrate that the model achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks. The model also exhibits good performance in terms of congestion control and path optimization and hence can be used effectively for intelligent routing in next-generation power communication networks.