Efficient Tensor Completion for Internet Traffic Data Recovery

In real-world traffic data, missing data is inevitable for traffic collection problem and some other reasons. Therefore, it is increasingly critical to complete the whole traffic data. To make full use of hidden spatial-temporal structures of the Internet traffic data, we extend to traffic matrix (user, server) to a three-way tensor (user, server, time). This paper proposed a novel algorithm to recover the missing data, named TSVT (Tucker decomposition and tensor singular value thresholding). We exploit the proposed the Tucker decomposition and low rank tensor completion (LRTC). Based on LRTC, we extend the known singular value thresholding (SVT) to the tensor case, and combine Tucker decomposition to optimize the performance of the algorithm. Based on Internet traffic data, we model two different tensors, and apply our TSVT to infer the unobserved data of the traffic. Furthermore, we apply TSVT algorithm to our Internet data. Numerical experiments on Synthetic data verify our method and the application for completing DPI traffic data proves the effectiveness of the method.