Multi-Link Fragmentation-Aware Deep Reinforcement Learning RSA Algorithm in Elastic Optical Network

Deep reinforcement learning has been extensively applied for resource allocation in elastic optical networks. However, many studies focus on link-level state analysis and rarely discuss the influence between links, which may affect the performance of allocation algorithms. In this paper, we propose a multi-link fragmentation deep reinforcement learning-based routing and spectrum allocation algorithm (MFDRL-RSA). We number the links using a breadth-first numbering algorithm. Based on the numbering results, high-frequency links are selected to construct the network state matrix that reflects the resource distribution. According to the state matrix, we calculate a multi-link fragmentation degree, quantifying resource fragmentation within a representative subset of network. The MFDRL-RSA algorithm enhances the accuracy of the agent’s decision-making by incorporating it into the reward function, thereby improving its performance in routing decisions, which contributes to the overall allocation performance. Simulation results show that MFDRL-RSA achieves lower blocking rates compared to the reference algorithms, with reductions of 16.34%, 13.01%, and 7.42% in the NSFNET network and 19.33%, 15.17%, and 9.95% in the Cost-239 network. It also improves spectrum utilization by 12.28%, 9.83%, and 6.32% in NSFNET and by 13.92%, 11.55%, and 8.26% in Cost-239.