基于强化学习的空分复用弹性光网络上的网络切片嵌入
RL-based Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks
- The University of Texas at Dallas（德克萨斯大学达拉斯分校）
- San Jose State University（圣何塞州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对SDM-EONs网络切片嵌入问题，提出路径约束强化学习框架及PPO-Full算法，联合选择计算节点与路由路径，高负载下请求接受率较基线显著提升。
AI中文摘要：
空分复用弹性光网络（SDM-EONs）上的网络切片需要联合管理频谱、空间核与计算资源，现有诸多研究忽视了这种耦合关系，将计算节点放置与路由、频谱决策分开处理。这种脱节可能导致频谱沿某条路径分配后，请求因该路径上计算资源不足而失败，也可能造成计算资源分配时未考虑计算节点间路径上的频谱资源可用性。我们提出一种路径约束强化学习框架，该框架同时处理计算节点选择与路由频谱及核心分配（RMCSA），兼顾两类资源，并将强化学习智能体的动作空间限制在请求端点间的k条最短路径上的节点。训练过程采用奖励塑形以提升高负载下的鲁棒性。我们提出PPO-Full（全近端策略优化），该算法通过多维动作空间同时选择计算节点与路由路径，在24节点USNET拓扑、热点流量场景下，与基于距离的启发式算法、贪心基线及解耦式VONE-DRL基线进行对比。结果表明，在高负载下，其请求接受率较所有基线均有持续提升，且随着流量强度增加，提升效果愈发显著。
英文摘要：
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.
