A review of machine learning-based failure management in optical networks

Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for performing comprehensive data analysis and complex network management and is widely utilized for failure management in optical networks to revolutionize the conventional manual methods. In this study, the background of failure management is introduced, where typical failure tasks, physical objects, ML algorithms, data sources, and extracted information are illustrated in detail. An overview of the applications of ML in failure management is provided in terms of alarm analysis, failure prediction, failure detection, failure localization, and failure identification. Finally, the future directions on ML for failure management are discussed from the perspective of data, model, task, and emerging techniques.

Automated summary

Failure management is essential for secure operation and risk mitigation in optical networks. Machine learning (ML) is a powerful technique that revolutionizes traditional manual methods. This study provides an overview of the background of failure management and details the applications of ML in this field, as well as discusses future directions.