Proactive Failure Management Methods for Coherent Optical Networks

相干光网络的主动故障管理方法

• 批准号: 530336-2018
• 负责人: Tremblay, Christine
• 金额: $ 1.82万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667075
• 关键词: Proactive; Failure; Management; Methods; Coherent

Current fault management strategies are essentially reactive and consist in implementing in the network control**system algorithms for network reconfiguration which take action only after the failures. Proactive approaches**that allow forecasting equipment failures in optical networks have been explored in the last few years.**Equipment failure prediction methods using machine learning (ML) and time series in optical networks have**been proposed. Different ML techniques have been explored in the last two years to provide cognitive solutions**for failure detection in optical networks. A failure identification and localization tool based on Bayesian**inference, as well as methods for bit error rate (BER) degradation detection and failure detection in optical**networks, have been proposed recently. However, most of the research on cognitive optical networking is at a**very early stage of exploration and based on synthetic or lab performance data in the absence of field**performance data. Therefore, the applicability of these new proactive fault management strategies in real**network deployment conditions is yet to be demonstrated. The development of predictive system analysis tools**would provide network operators with an advantage in making their optical network more reliable and would**also provide an opportunity to reduce their operational expenditures through automated network control**system.**The proposed project will explore proactive fault management strategies in coherent optical networks based on**machine learning techniques. The project will leverage the research activities on cognitive optical networking**at the Network Technology Lab in the last two years. The project will consist in evaluating different ML**algorithms, with the objective to determine the most promising approaches for fault prediction. The innovative**ML methods will be evaluated according to the following metrics: prediction accuracy, computing time and**scalability. Synthetic BER data will be used for evaluating the performance of the ML-based fault predictors.

目前的故障管理策略基本上是反应性的，并且包括在网络控制系统中执行用于网络重构的算法，该算法仅在故障之后才采取行动。在过去几年中，已经探索了允许预测光网络中的设备故障的主动方法 **。提出了利用机器学习（ML）和时间序列进行光网络设备故障预测的方法。在过去的两年中，人们探索了不同的ML技术，为光网络中的故障检测提供认知解决方案。最近提出了一种基于贝叶斯推理的故障识别和定位工具，以及用于光 ** 网络中的误码率（BER）退化检测和故障检测的方法。然而，大多数关于认知光网络的研究都处于探索的早期阶段，并且在缺乏现场性能数据的情况下基于合成或实验室性能数据。因此，这些新的主动故障管理策略在真实的 ** 网络部署条件下的适用性还有待证明。预测性系统分析工具的开发 ** 将为网络运营商提供优势，使其光网络更加可靠，** 还将提供机会，通过自动化网络控制 ** 系统减少运营支出。拟议的项目将探索基于 ** 机器学习技术的相干光网络中的主动故障管理策略。该项目将利用网络技术实验室在过去两年中对认知光网络 ** 的研究活动。该项目将包括评估不同的ML** 算法，目的是确定最有前途的故障预测方法。创新的ML方法将根据以下指标进行评估：预测准确性，计算时间和 ** 可扩展性。合成BER数据将用于评估基于ML的故障预测器的性能。