The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O dimensionamento da rede é uma questão importante para fornecer serviços de comunicação estáveis e ricos em QoS. Uma estimativa confiável das larguras de banda dos links entre o caminho ponta a ponta é um primeiro passo para o dimensionamento da rede. Caminhochar é uma dessas ferramentas para estimativa de largura de banda para cada link entre dois hosts finais. No entanto, caminhochar ainda tem vários problemas. Se forem incluídos erros inesperadamente grandes ou se houver alternância de rotas durante a medição, a estimativa obtida estará muito longe da correta. Investigamos o método para eliminar esses erros na estimativa da largura de banda. Para aumentar a confiabilidade da estimativa, o intervalo de confiança para a largura de banda estimada é importante. Para tanto, duas abordagens, abordagens paramétricas e não paramétricas, são investigadas para adicionar os intervalos de confiança. Outra questão importante é o método para controlar o período de medição para eliminar os custos indiretos de medição. Neste artigo, propomos um método de medição para controlar adaptativamente o número de conjuntos de dados de medição. Através de resultados experimentais, mostramos que nossas abordagens estatísticas podem fornecer estimativas robustas independentemente das condições da rede.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
Kazumine MATOBA, Shingo ATA, Masayuki MURATA, "Improving Bandwidth Estimation for Internet Links by Statistical Methods" in IEICE TRANSACTIONS on Communications,
vol. E84-B, no. 6, pp. 1521-1531, June 2001, doi: .
Abstract: Network dimensioning is an important issue to provide stable and QoS-rich communication services. A reliable estimation of bandwidths of links between the end-to-end path is a first step towards the network dimensioning. Pathchar is one of such tools for the bandwidth estimation for every link between two end hosts. However, pathchar still has several problems. If unexpectedly large errors are included or if route alternation is present during the measurement, the obtained estimation is much far from the correct one. We investigate the method to eliminate those errors in estimating the bandwidth. To increase the reliability on the estimation, the confidence interval for the estimated bandwidth is important. For this purpose, two approaches, parametric and nonparametric approaches, are investigated to add the confidence intervals. Another important issue is the method for controlling the measurement period to eliminate the measurement overheads. In this paper, we propose a measurement method to adaptively control the number of measurement data sets. Through experimental results, we show that our statistical approaches can provide the robust estimation regardless of the network conditions.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e84-b_6_1521/_p
Copiar
@ARTICLE{e84-b_6_1521,
author={Kazumine MATOBA, Shingo ATA, Masayuki MURATA, },
journal={IEICE TRANSACTIONS on Communications},
title={Improving Bandwidth Estimation for Internet Links by Statistical Methods},
year={2001},
volume={E84-B},
number={6},
pages={1521-1531},
abstract={Network dimensioning is an important issue to provide stable and QoS-rich communication services. A reliable estimation of bandwidths of links between the end-to-end path is a first step towards the network dimensioning. Pathchar is one of such tools for the bandwidth estimation for every link between two end hosts. However, pathchar still has several problems. If unexpectedly large errors are included or if route alternation is present during the measurement, the obtained estimation is much far from the correct one. We investigate the method to eliminate those errors in estimating the bandwidth. To increase the reliability on the estimation, the confidence interval for the estimated bandwidth is important. For this purpose, two approaches, parametric and nonparametric approaches, are investigated to add the confidence intervals. Another important issue is the method for controlling the measurement period to eliminate the measurement overheads. In this paper, we propose a measurement method to adaptively control the number of measurement data sets. Through experimental results, we show that our statistical approaches can provide the robust estimation regardless of the network conditions.},
keywords={},
doi={},
ISSN={},
month={June},}
Copiar
TY - JOUR
TI - Improving Bandwidth Estimation for Internet Links by Statistical Methods
T2 - IEICE TRANSACTIONS on Communications
SP - 1521
EP - 1531
AU - Kazumine MATOBA
AU - Shingo ATA
AU - Masayuki MURATA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E84-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2001
AB - Network dimensioning is an important issue to provide stable and QoS-rich communication services. A reliable estimation of bandwidths of links between the end-to-end path is a first step towards the network dimensioning. Pathchar is one of such tools for the bandwidth estimation for every link between two end hosts. However, pathchar still has several problems. If unexpectedly large errors are included or if route alternation is present during the measurement, the obtained estimation is much far from the correct one. We investigate the method to eliminate those errors in estimating the bandwidth. To increase the reliability on the estimation, the confidence interval for the estimated bandwidth is important. For this purpose, two approaches, parametric and nonparametric approaches, are investigated to add the confidence intervals. Another important issue is the method for controlling the measurement period to eliminate the measurement overheads. In this paper, we propose a measurement method to adaptively control the number of measurement data sets. Through experimental results, we show that our statistical approaches can provide the robust estimation regardless of the network conditions.
ER -