Abstract

In optical transport networks, failure localization is usually triggered as a response to alarms and significant anomalous behaviors. However, the recent evolution of network control and management leveraging software-defined networking (SDN) and streaming-based telemetry opens up new possibilities for automated methods that can localize even subtle anomalies, the so-called soft failures. This paper reports the experimental demonstration of a machine-learning-based soft-failure localization framework in a small-scale laboratory setup. The SDN telemetry setup includes ONOS-controlled transponders using NETCONF and an optical line system (OLS) providing telemetry via an OLS domain controller. A shallow artificial neural network (ANN) accomplishes ML-based failure localization with principal component analysis to reduce non-essential information. The ANN is trained by combining field data and synthetic data generated in a digital network twin. The field data trains the ANN to tolerate statistical variations in the network telemetry without failures, while the synthetic data generates artificial single-failure scenarios. We show that the soft-failure localization scheme successfully pinpoints the faulty element in all single failures generated in transponders, fibers, and amplifiers. We also demonstrate the system’s ability to deal with double-failure scenarios.

Digital-twin-assisted meta learning for soft-failure localization in ROADM-based optical networks

Ruikun Wang, Jiawei Zhang, Zhiqun Gu, Memedhe Ibrahimi, Bojun Zhang, Francesco Musumeci, Massimo Tornatore, and Yuefeng Ji
J. Opt. Commun. Netw. 16(7) C11-C19 (2024)

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)

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 framework for timely soft-failure detection and localization in elastic optical networks

Sadananda Behera, Tania Panayiotou, and Georgios Ellinas
J. Opt. Commun. Netw. 15(10) E74-E85 (2023)

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)