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

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.