Network Latency Estimation with Graph-Laplacian Regularization Tensor Completion

In recent years, with the growing prevalence of personal devices, network latency of devices has drawn much attention due to its significant influence on user experience. Thus network latency estimation is considered to be an important index for network performance evaluation. However, the existing works on network latency estimation are unable to achieve satisfactory estimation accuracy due to the adoption of the conventional matrix or tensor model. In this paper, we construct a novel tensor model based on tensor-SVD for network latency data to make full use of its potential latent factors. Besides, we also propose a graph-laplacian regularization tensor completion algorithm (GLRTC), which mines the underlying spatial information by introducing graph-laplacian regularization constraints to improve the recovery performance. Finally, we conduct extensive simulations on the real-world latency dataset and demonstrate the effectiveness of the proposed algorithm. Comparing with the existing approaches, the proposed algorithm achieves significant improvement in terms of recovery accuracy.