# A Review of Machine Learning-based Failure Management in Optical Networks

arXiv: https://arxiv.org/abs/2208.10677 · HTML: https://mlsanity.com/paper/2208.10677/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2022.

## Built on by (most cited first, 4 of 4)

- [Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent](https://mlsanity.com/paper/2303.02868/) (2023)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [From Data to Decision: A Multi-Stage Framework for Class Imbalance Mitigation in Optical Network Failure Analysis](https://mlsanity.com/paper/2509.00057/) (2025)
- [Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management](https://mlsanity.com/paper/2609.32226/) (2026)

## Abstract (arXiv)

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 source, 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.