Lightweight Trilinear Pooling based Tensor Completion for Network Traffic Monitoring

Abstract

Network traffic engineering and anomaly detection rely heavily on network traffic measurement. Due to the lack of infrastructure to measure all points of interest, the high measurement cost, and the unavoidable transmission loss, network monitoring systems suffer from the problem that the network traffic data are incomplete with only a subset of paths or time slots measured. Recent studies show that tensor completion can be applied to infer the missing traffic data from partial measurements. Although promising, the interaction model adopted in current tensor completion algorithms can only capture linear and simple correlations in the traffic data, which compromises the recovery performance. To solve the problem, we propose a new tensor completion scheme based on Lightweight Trilinear Pooling, which designs (1) a Trilinear Pooling, a new multi-modal fusion method to model the interaction function to capture the complex correlations, (2) a low-rank decomposition based neural network compression method to reduce the storage and computation complexity, (3) an attention enhanced LSTM to encode and incorporate the temporal patterns in the tensor completion scheme. The extensive experiments on three real-world network traffic datasets demonstrate that our scheme can significantly reduce the error in missing data recovery with fast speed using small storage.