Spatiotemporal-Enhanced Recurrent Neural Network for Network Traffic Prediction

Abstract

Network traffic prediction can serve as a proactive approach for network resource planning, allocation, and management. Besides, it can also be applied for load generation in digital twin networks (DTNs). This paper focuses on background traffic prediction of typical local area networks (LANs), which is vital for synchronous traffic generation in DTN. Conventional traffic prediction models are firstly reviewed. The challenges of DTN traffic prediction are analyzed. On that basis, a spatiotemporal-enhanced recurrent neural network (RNN) based approach is elaborated to accurately predict the background traffic matrices of target LANs. Experiments compare this proposed model with four baseline models, including LSTM, CNN-LSTM, ConvLSTM, and PredRNN. The results turn out that the spatiotemporal-enhanced RNN model outperforms the baselines on accuracy. In particular, it can decrease the MSE of PredRNN more than 18%, with acceptable efficiency degradation.