Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation

TL;DR

This paper introduces a novel tensor completion method using leverage sampling and tensor QR decomposition, significantly speeding up network latency estimation while maintaining high accuracy.

Contribution

It proposes a new tensor completion approach that replaces t-SVD with tensor CSVD-QR and integrates tensor QR into ADMM for faster, accurate network latency estimation.

Findings

Our method outperforms existing algorithms in speed.

It achieves comparable or better accuracy.

Numerical experiments validate the efficiency and effectiveness.

Abstract

In this paper, we consider the network latency estimation, which has been an important metric for network performance. However, a large scale of network latency estimation requires a lot of computing time. Therefore, we propose a new method that is much faster and maintains high accuracy. The data structure of network nodes can form a matrix, and the tensor model can be formed by introducing the time dimension. Thus, the entire problem can be be summarized as a tensor completion problem. The main idea of our method is improving the tensor leverage sampling strategy and introduce tensor QR decomposition into tensor completion. To achieve faster tensor leverage sampling, we replace tensor singular decomposition (t-SVD) with tensor CSVD-QR to appoximate t-SVD. To achieve faster completion for incomplete tensor, we use the tensor $L_{2,1}$-norm rather than traditional tensor nuclear norm. Furthermore, we introduce tensor QR decomposition into alternating direction method of multipliers (ADMM) framework. Numerical experiments witness that our method is faster than state-of-art algorithms with satisfactory accuracy.