Abstract

We propose a reliable and easy-to-deploy method for localizing failures in optical networks with low added complexity. It essentially relies on the aggregation of raw monitoring data from coherent transponders; data readily accessible through streaming telemetry in modern optical networks. We first verify the reliability of our algorithm through extensive simulations over a European topology under realistic conditions. Then, we apply it to field data from a backbone network carrying live traffic. Here, we show how failure localization allows to generate strategic network health insights regarding link failure probabilities and mean time to repair. We notably underline the involvement of Q-drops in strong and localized deviations of both parameters.

Suspect fault screen assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks

Ruikun Wang, Jiawei Zhang, Shuangyi Yan, Chuidian Zeng, Hao Yu, Zhiqun Gu, Bojun Zhang, Tarik Taleb, and Yuefeng Ji
J. Opt. Commun. Netw. 15(7) C88-C99 (2023)

Machine-learning-based soft-failure localization with partial software-defined networking telemetry

Kayol S. Mayer, Jonathan A. Soares, Rossano P. Pinto, Christian E. Rothenberg, Dalton S. Arantes, and Darli A. A. Mello
J. Opt. Commun. Netw. 13(10) E122-E131 (2021)

Machine learning models for alarm classification and failure localization in optical transport networks

Jatin Babbar, Ahmed Triki, Reda Ayassi, and Maxime Laye
J. Opt. Commun. Netw. 14(8) 621-628 (2022)

Leveraging Statistical Machine Learning to Address Failure Localization in Optical Networks

T. Panayiotou, S. P. Chatzis, and G. Ellinas
J. Opt. Commun. Netw. 10(3) 162-173 (2018)

Multi-link Failure Localization via Monitoring Bursts

Mohammed L. Ali, Pin-Han Ho, János Tapolcai, and Suresh Subramaniam
J. Opt. Commun. Netw. 6(11) 952-964 (2014)