RecMon: A Deep Learning-based Data Recovery System for Network Monitoring

Network monitoring systems struggle with the issue that the measurement data is incomplete, with only a subset of origin-destination (OD) pairs or time slots observed, due to the high deployment and measurement cost. Recent studies show that the missing data can be inferred from partial measurements using neural network models and tensor methods. However, these recovery approaches fail to achieve accuracy, adaptability and high speed, simultaneously. In this paper, we propose RecMon, a deep learning-based data recovery system that satisfies the above three criteria. A global spatio-temporal attention mechanism and a data augmentation algorithm are proposed to improve the recovery accuracy. A semi-supervised learning-based scheme is devised for fast and effective model updates. We conduct extensive experiments on three real-world datasets to compare RecMon with four state-of-the-art methods in terms of online recovery performance. The experimental results show that RecMon can adapt to the latest state of the network and accurately recover network measurement data in less than 100 milliseconds. When 90% of the data is missing, the recovery accuracy of RecMon improves over the strongest baseline method by 22.7%, 16.0%, and 8.2% in the three datasets, respectively.