Abstract Alarm prediction in optical networks focuses on forecasting network failure from the state of equipment and links. The existing prediction methods usually rely on large amounts of data, while centralizing all processes in the network controller or management system may increase the system burden. In this paper, a novel method is proposed in self-optimized optical networks (SOON) to implement alarm classification prediction based on cross-layer artificial intelligence (AI) architecture. We adopts alarm risk assessment and data augmentation with synthetic minority oversampling technique (SMOTE). As a distributed system, cross-layer AI completes decomposed functions by using interactions between different AI engines. With the help of the controller system, functions can be executed in order. The amount of data required for prediction is far less than other methods. The validity of the method is proved using the collected data from a commercial synchronous digital hierarchy (SDH) network. Experimental results show that promising precision (95%) can be achieved in predicting the optical equipment alarms.

中文翻译：

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自优化光网络中基于跨层人工智能交互的告警分类预测（SOON）

摘要 光网络告警预测的重点是从设备和链路状态预测网络故障。现有的预测方法通常依赖于大量数据，而将所有流程集中在网络控制器或管理系统中可能会增加系统负担。在本文中，提出了一种在自优化光网络（SOON）中基于跨层人工智能（AI）架构实现告警分类预测的新方法。我们采用合成少数过采样技术（SMOTE）进行警报风险评估和数据增强。跨层AI作为分布式系统，通过不同AI引擎之间的交互来完成分解的功能。在控制器系统的帮助下，功能可以按顺序执行。预测所需的数据量远远少于其他方法。使用从商业同步数字层次 (SDH) 网络收集的数据证明了该方法的有效性。实验结果表明，在预测光学设备警报方面可以达到有希望的精度（95%）。