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.
翻译:失灵管理在光学网络中起着重要作用,它确保安全运行,减轻潜在风险,并主动实施保护; 机器学习(ML)被认为是进行全面数据分析和复杂的网络管理的一个极为有力的技术,被广泛用于光学网络的失灵管理,使传统的手工方法发生革命性变化; 在这项研究中,引入了失灵管理的背景,其中详细介绍了典型的失灵任务、实物物体、ML算法、数据来源和提取的信息; 以警报分析、故障预测、失灵检测、故障定位和故障识别为例,概述了失灵管理 ML的应用; 最后,从数据、模型、任务和新兴技术的角度讨论了失灵管理的未来方向。