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Demonstration of ML-Assisted Soft-Failure Localization Based on Network Digital Twins

2022; Institute of Electrical and Electronics Engineers; Volume: 40; Issue: 14 Linguagem: Inglês

10.1109/jlt.2022.3170278

1558-2213

Kayol S. Mayer, Rossano P. Pinto, Jonathan Aguiar Soares, Dalton S. Arantes, Christian Esteve Rothenberg, Vinicius Cavalcante, Leonardo Luis Santos, Filipe D. Moraes, Darli A. A. Mello,

Optical Network Technologies

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.