Name
Abstract | ||
|---|---|---|
When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. Based on the sparse recovery technique reported in [Wang et al. (2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining a similar recovery performance, which shows its capability to cope with large-scale networks. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Networking and Internet Architecture | Journal |
Volume | Citations | PageRank |
abs/1812.00369 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Ting Wei | 1 | 0 | 0.68 |
| Sung-Hsien Hsieh | 2 | 0 | 0.68 |
| Wen-Liang Hwang | 3 | 32 | 6.93 |
| Chung-shou Liao | 4 | 320 | 20.95 |
| Chun-shien Lu | 5 | 1238 | 104.71 |