Learning Long- and Short-Term Temporal Patterns for ML-Driven Fault Management in Optical Communication Networks

Abstract

The deployment of 5G and network slicing has challenged the current network management requirements, triggering the need for programmable and software-driven architectures. Thus, automated real-time fault management for self-managed networks with machine learning and artificial intelligence at the forefront has become necessary. This is especially the case of optical communication systems, accountable for most of the data traffic worldwide. This study introduces the application of a novel failure detection and localization framework capable of forecasting failures in optical systems based on an unsupervised learning strategy. In this approach, the Long- and Short-term Time-series Network (LSTNet) is exploited for modeling the normal behavior of optical systems. Then, failure conditions are properly forecast without explicitly training the model for such cases, easing the data acquisition process. Later, forecast values and actual measurements from optical equipment are used to derive an outlier detection method to detect and locate failures to improve the decision-making process at the network orchestrator. Laboratory experiments comparing the proposed approach with the Recurrent and Long Short-Term Memory models in terms of failure detection and forecasting performance show that using the LSTNet reduces the mean squared errors in 95% for unseen data, indicating robustness and suitability for real-world environments.