Machine-Learning-based Alarm Prediction with GANs-based Self-Optimizing Data Augmentation in Large-Scale Optical Transport Networks

Abstract

Machine learning (ML) is an effective solution to deal with some networking issues in large scale optical transport networks. Alarm (caused by failure, disaster, etc.) prediction is a typical and important use-case, where ML algorithm can work to predict events in advance. By accurate prediction, network administrators can be guided to undertake preventive measures ahead of time, thus avoiding the adverse effect. For training and testing of such event prediction models, it’s first and foremost to build a high-quality data set from real networks. However, the data collected from large-scale optical transport networks are inferior in quality and demand prompt pre-processing, for they are sometimes invalid, incomplete, inconsistent, and even inaccurate. After data pre-processing, there is only a small amount of available data left to build an ML data set, which extremely limits the performance of the alarm prediction model. This paper proposes a machine-learning-based alarm prediction method using self-optimizing data augmentation based on generative adversarial networks (GANs) for optical transport networks. Experimental results on a commercial backbone synchronous digital hierarchy (SDH) network with 274 nodes and 487 links demonstrate that the proposed method can achieve high accuracy for alarm prediction.