Forecasting Loss of Signal in Optical Networks with Machine Learning
This addresses cost reduction for optical network operators by enabling proactive maintenance, though it is incremental as it applies existing ML methods to a specific domain problem.
The study tackled forecasting Loss of Signal events in optical networks using machine learning on real-world data from six networks, achieving good precision 1-7 days in advance but with low recall, and showed that training on multiple networks improves precision and allows a single model to generalize across networks and facility types.
Loss of Signal (LOS) represents a significant cost for operators of optical networks. By studying large sets of real-world Performance Monitoring (PM) data collected from six international optical networks, we find that it is possible to forecast LOS events with good precision 1-7 days before they occur, albeit at relatively low recall, with supervised machine learning (ML). Our study covers twelve facility types, including 100G lines and ETH10G clients. We show that the precision for a given network improves when training on multiple networks simultaneously relative to training on an individual network. Furthermore, we show that it is possible to forecast LOS from all facility types and all networks with a single model, whereas fine-tuning for a particular facility or network only brings modest improvements. Hence our ML models remain effective for optical networks previously unknown to the model, which makes them usable for commercial applications.