Name
Title | ||
|---|---|---|
Time-varying network internal loss inference based on unicast end-to-end measurements |
Abstract | ||
|---|---|---|
Most of methods for network link performance parameters inference are under the assumption that the link states are stationary during measurement period, as a result, the time-varying characteristics of link state can not be obtained. In this paper, we present a novel nonstationary internal loss tomography method to infer time-varying link loss characteristic. The method is based on improved three-packet stripe, which can provide more accurate unicast end-to-end measurements. The ns-2 simulation shows good performance of improved probe, and effectiveness of time-varying internal loss inference method in tracking variation of link loss. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISCC.2009.5202300 | ISCC |
Keywords | DocType | ISSN |
unicast end-to-end measurement,network link performance parameter inference,tomography,tracking variation,time varying network internal loss inference,telecommunication network management,time varying link loss,nonstationary internal loss tomography method to,losses,time measurement,unicast,mathematical model,taylor series,maximum likelihood estimation | Conference | 1530-1346 E-ISBN : 978-1-4244-4671-1 |
ISBN | Citations | PageRank |
978-1-4244-4671-1 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaolei Fei | 1 | 3 | 3.14 |
| Hu Guangmin | 2 | 7 | 0.89 |
| Qian Feng | 3 | 1 | 1.06 |