Failure Management Overview in Optical Networks

Conventional optical networks are limited by static operational methods, hindering scalability and effectiveness. As networks operate with reduced margins to maximize resource utilization, the risk of hard failures increases, necessitating efficient failure prediction systems and accurate quality of transmission (QoT) estimation. Effective management requires the detection of soft failures, accurate bit error rate (BER) predictions, and dynamic network operations to maintain minimal margins. Machine learning (ML) offers promising solutions for automating these tasks, significantly enhancing failure management and network reliability. This article provides an extensive overview of ML techniques applied to optical networks, specifically focusing on failure management. Key ML techniques discussed include network kriging (NK) for performance estimation and failure localization, support vector machine (SVM) for classification tasks, convolutional neural networks (CNNs) for signal analysis and soft failure identification, and generative adversarial networks (GANs) for synthetic data generation and soft failure detection. It also explores the application of artificial neural networks (ANNs), autoencoders (AEs), Gaussian process (GP), long shortterm memory (LSTM), and gated recurrent units (GRUs) in optical networks. The paper surveys ML techniques for earlywarning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. Emphasizing automations, it discusses how ML algorithms can streamline failure management processes, reducing manual intervention and service disruptions. The potential of large language models (LLMs) and digital twins (DTs) for further advancements in automating failure management, optimizing performance, and network optimization in optical networks is also examined. LLMs significantly advance network management by improving network design, diagnosis, security, and autonomous optimization through the integration comprehensive domain resources and intelligent agents. These advancements are paving the way towards achieving artificial general intelligence and fully automated optical network management.