Network Traffic Prediction Based on the Feature of Newly-Generated Network Flows
Shaohe Li, Junping Song, Luyang Xu, Yahui Hu, Wanming Luo, Xu Zhou
Abstract
Network traffic prediction is essential for intelligent network management, such as resource reservation and burst warning. Existing prediction approaches are vulnerable in accurately capturing the sudden surge or plunge, uniformly denoted as the traffic burst. To solve this problem, we extract the time series of the number of newly-generated network flows (NoNGF) from the network flow information, explaining the intrinsic mechanism of network traffic bursts. We use time-lagged cross-correlation analysis to identify directionality between the NoNGF series and traffic series. It proves that we can perceive the future fluctuation and burst of network traffic by NoNGF in advance. The comprehensive prediction experiments of the whole network traffic and three application-level network traffic demonstrate that our proposed approach exhibits a significant performance improvement over the original LSTM and TCN models. Our approach can accurately capture the moment of network burst and the predicted value much more precisely when the burst occurs. In summary, our proposed traffic prediction based on NoNGF can significantly improve the prediction accuracy, especially for network burst traffic.