Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation
This work addresses network performance monitoring by providing a faster method for latency estimation, though it appears incremental as it builds on existing tensor completion techniques.
The paper tackles the problem of network latency estimation by formulating it as a tensor completion task, proposing a method that improves tensor leverage sampling and incorporates tensor QR decomposition to achieve faster computation while maintaining high accuracy, with numerical experiments showing it outperforms state-of-the-art algorithms in speed.
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.