Name
Abstract | ||
|---|---|---|
Network tomography is an appealing technology to infer link delay distributions since it only relies on end-to-end measurements. However, most approaches in network delay tomography are usually computationally intractable. In this letter, we propose a Fast link Delay distribution Inference algorithm (FDI). It estimates the node cumulative delay distributions by explicit computations based on a subtree-partitioning technique, and then derives the individual link delay distributions from the estimated cumulative delay distributions. Furthermore, a novel discrete delay model where each link has a different bin size is proposed to efficiently capture the essential characteristics of the link delay. Combining with the variable bin size model, FDI can identify the characteristics of the network-internal link delay quickly and accurately. Simulation results validate the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1587/transcom.E96.B.504 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | Field | DocType |
network tomography, link delay distribution inference, variable bin size model | Mathematical optimization,Bin,Inference,Computer science,Algorithm,Network tomography,Distributed computing | Journal |
Volume | Issue | ISSN |
E96B | 2 | 0916-8516 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyong Zhang | 1 | 0 | 0.34 |
| Gaolei Fei | 2 | 3 | 3.14 |
| Shenli Pan | 3 | 2 | 0.71 |
| Fucai Yu | 4 | 216 | 21.85 |
| Guang-min Hu | 5 | 87 | 19.78 |