发表机构
San Jose State University; The University of Texas at Dallas（圣何塞州立大学; 德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多域光网络中外部实体对内部信息有限可见性导致的E2E性能保障难题，提出基于DRL的自适应监测路径选择算法，构建两类互补优化问题，经GNPy仿真验证其性能优于基线算法。
AI 中文摘要
多域光网络中的网络切片可为高带宽虚拟专用网络实现资源隔离，但由于各域对外部实体隐藏内部拓扑和性能指标，保证端到端（E2E）性能仍具挑战性。在这种有限可见性下，控制功能（切片协调器）需选择E2E监测路径以检测和定位链路故障。本文构建两个互补优化问题：初始监测路径选择问题和渐进监测路径选择问题，二者共同实现切片协调器层面的闭环自主监测。初始问题在无先验信息时最大化故障覆盖范围，渐进问题则在先前监测识别的疑似故障位置周围集中额外路径。我们提出一种深度强化学习（DRL）算法，可在每个阶段自适应选择最优监测路径集。使用GNPy光网络模拟器开展的实验表明，本文方法在两个问题上均优于基线算法，且在初始选择中实现接近最优的定位，证明了AI驱动的闭环故障定位在未来多域光网络架构中的可行性。
英文摘要
Network slicing over multi-domain optical networks enables resource isolation for high-bandwidth, virtually-dedicated networks. However, guaranteeing end-to-end (E2E) performance remains challenging when domains withhold internal topology and performance metrics from external entities. Under this limited visibility, a control function (slice coordinator) needs to select E2E monitoring paths to detect and localize link failures. This paper formulates two complementary optimization problems, the initial and progressive monitoring path selection problems, which together realize closed-loop autonomous monitoring at the slice coordinator level. The initial problem maximizes failure coverage without prior information, while the progressive problem concentrates additional paths around suspected failure locations identified from prior monitoring. We propose a deep reinforcement learning (DRL) algorithm that adaptively selects an optimal set of monitoring paths for each phase. Experiments using the GNPy optical network simulator demonstrate that our approach outperforms baseline algorithms for both problems and achieves near-optimal localization in the initial selection, suggesting the feasibility of AI-driven closed-loop failure localization in future multi-domain optical network architectures.
CommentsTo be published in Journal of Optical Communications and Networking
