RL-based Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

2026-08-29

AI Summary

本文针对SDM-EONs网络切片中计算资源与路由光谱资源管理脱节的问题，提出了一种路径约束的强化学习框架PPO-Full，通过联合优化计算节点选择和路由路径，在高负载下提高了请求接受率。

Abstract

Network slicing over space-division-multiplexed elastic optical networks (SDM-EONs) requires jointly managing spectrum, spatial cores, and compute resources, a coupling that many existing studies ignore by treating compute placement independently from routing and spectrum decisions. This disconnect can cause the spectrum to be allocated along a path, only for the request to fail due to insufficient compute resources along the path, or may result in compute resources being allocated without consideration for spectrum resource availability on the path between compute nodes. We propose a path-constrained reinforcement learning framework that addresses compute node selection and RMCSA, being aware of both resources, restricting the RL agent's action space to nodes along $k$-shortest paths between request endpoints. Training incorporates reward shaping to improve robustness under high load. We propose PPO-Full (Proximal Policy Optimization-Full), which jointly selects compute nodes and routing paths via a multi-dimensional action space, against distance-based heuristics, a greedy baseline, and a decoupled VONE-DRL baseline on a 24-node USNET topology under hotspot traffic conditions. Results demonstrate consistent improvements in acceptance rate over all baselines at high load, with gains becoming more pronounced as traffic intensity increases.

Problem

• Network Slicing
• Space Division Multiplexed Elastic Optical Networks
• Spectrum Management
• Compute Resources

Innovation

• path-constrained reinforcement learning
• RMCSA
• reward shaping
• PPO-Full