A probabilistic approach to fault diagnosis in linear lightwave networks

10.1109/49.257935IEEE Journal on Selected Areas in Communications1191438-1448ISAC

Monitoring Cycle Design for Fast Link Failure Localization in All-Optical Networks

A monitoring cycle (m-cycle) is a preconfigured optical loop-back connection of supervisory wavelengths with a dedicated monitor. In an all-optical network (AON), if a link fails, the supervisory optical signals in a set of m-cycles covering this link will be disrupted. The link failure can be localized using the alarm code generated by the corresponding monitors. In this paper, we first formulate an optimal integer linear program (ILP) for m-cycle design. The objective is to minimize the monitoring cost which consists of the monitor cost and the bandwidth cost (i.e., supervisory wavelength-links). To reduce the ILP running time, a heuristic ILP is also formulated. To the best of our survey, this is the first effort in m-cycle design using ILP, and it leads to two contributions: 1) nonsimple m-cycles are considered; and 2) an efficient tradeoff is allowed between the monitor cost and the bandwidth cost. Numerical results show that our ILP-based approach outperforms the existing m-cycle design algorithms with a significant performance gain.published_or_final_versio

Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability

Neighborhood Failure Localization in All-Optical Networks via Monitoring Trails

Shared protection, such as failure dependent protection (FDP), is well recognized for its outstanding capacity efficiency in all-optical mesh networks, at the expense of lengthy restoration time due to multi-hop signaling mechanisms for failure localization, notification, and device configuration. This paper investigates a novel monitoring trail (m-trail) scenario, called Global Neighborhood Failure Localization (G-NFL), that aims to enable any shared protection scheme, including FDP, for achieving all-optical and ultra-fast failure restoration. We firstly define neighborhood of a node, which is a set of links whose failure states should be known to the node in restoration of the corresponding working lightpaths (W-LPs). By assuming every node can obtain the on-off status of traversing m-trails and W-LPs via lambda monitoring, the proposed G-NFL problem routes a set of m-trails such that each node can localize any failure in its neighborhood. Bound analysis is performed on the minimum bandwidth required for m-trails under the proposed G-NFL problem. Then a simple yet efficient heuristic approach is presented. Extensive simulation is conducted to verify the proposed G-NFL scenario under a number of different definitions of nodal neighborhood which concern the extent of dependency between the monitoring plane and data plane. The effect of reusing the spare capacity by FDP for supporting m-trails is examined. We conclude that the proposed G-NFL scenario enables a general shared protection scheme, toward signaling-free and ultra-fast failure restoration like p-Cycle, while achieving optimal capacity efficiency as FDP