Alarm classification prediction based on cross-layer artificial intelligence interaction in self-optimized optical networks (SOON)

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.