Forecasting Loss of Signal in Optical Networks with Machine Learning

TL;DR

This study demonstrates that supervised machine learning can predict Loss of Signal events in optical networks 1-7 days in advance with high precision across various facility types and networks, supporting commercial deployment.

Contribution

The paper introduces a machine learning approach capable of forecasting LOS events across multiple networks and facility types with a single, generalizable model, improving predictive accuracy.

Findings

Forecasting LOS with good precision 1-7 days ahead.

Training on multiple networks enhances prediction accuracy.

A single model can effectively predict LOS across all facility types and networks.

Abstract

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.