A review of machine learning-based failure management in optical networks
A review of machine learning-based failure management in optical networks作者机构：State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Photonics Research Center Department of Electrical Engineering The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2022年第65卷第11期
页 面：47-65页
核心收录：
学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported in part by National Key R&D Program of China (Grant No. 2019YFB1803502) National Natural Science Foundation of China (Grant Nos. 61975020, 61871415, 62171053)
主 题：machine learning artificial intelligence failure management optical network
摘 要：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.
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