A Tutorial on Machine Learning for Failure Management in Optical Networks
A Tutorial on Machine Learning for Failure Management in Optical Networks
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DOI：
10.1109/jlt.2019.2922586
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发表时间：
2019-08
影响因子：
4.7
通讯作者：
F. Musumeci;C. Rottondi;Giorgio Corani;S. Shahkarami;F. Cugini;M. Tornatore
中科院分区：
文献类型：
--
作者：
F. Musumeci;C. Rottondi;Giorgio Corani;S. Shahkarami;F. Cugini;M. Tornatore
故障管理在光网络中起着至关重要的作用，以避免服务中断，满足客户的服务水平协议。机器学习(ML)通过引入故障预测、检测、定位和识别的自动化方法，有望彻底改变光网络中传统的故障管理方法(主要是人工和人工驱动的)。本教程简单介绍了最近在光网络故障管理领域中应用的一些ML技术。然后介绍了对故障管理任务进行分类的分类方法，并讨论了ML在这些故障管理任务中的可能应用。最后，对于对更多实现细节感兴趣的读者，我们提供了如何解决实际故障管理任务的典型示例的逐步描述。
Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers’ service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.