Adaptive E2E Monitoring Path Selection with Deep Reinforcement Learning in Multi-domain Optical Networks

2026-10-07

AI Summary

This study addresses the challenges of end-to-end performance assurance and fault localization in multi-domain optical networks arising from cross-domain information isolation. We propose a closed-loop autonomous monitoring framework based on deep reinforcement learning. Specifically, a two-stage optimization model is developed to achieve initial full-coverage monitoring and progressively focus on suspected fault regions without prior knowledge, while integrating the GNPy optical simulator to drive adaptive monitoring path selection. Experimental results demonstrate that the proposed approach significantly outperforms baseline algorithms, achieving near-optimal fault localization accuracy. This work validates the feasibility of AI-empowered intelligent operation and maintenance for optical networks.

Abstract

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.

Problem

• multi-domain optical networks
• network slicing
• end-to-end monitoring path selection
• failure localization
• limited visibility

Innovation

• Deep Reinforcement Learning
• Multi-domain Optical Networks
• Monitoring Path Selection
• Network Slicing
• Closed-loop Fault Localization