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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
En tomographie de réseau, la plupart des travaux réalisés à ce jour reposent sur l'exploitation des corrélations au niveau des paquets de sonde pour déduire les taux de perte de liaison et les distributions de retards. Certains autres travaux se concentrent sur l'identification des liaisons encombrées à l'aide de mesures de bout en bout non corrélées et de la probabilité préalable d'encombrement des liaisons. Dans leurs travaux, les probabilités a priori sont identifiées par l'inversion matricielle avec un certain nombre d'instantanés de mesure, et l'algorithme permettant de trouver les liens encombrés est heuristique et non optimal. Dans cette lettre, nous présentons un nouvel estimateur des probabilités a priori qui est simple sur le plan informatique, étant une fonction explicite des instantanés de mesure. Avec ces probabilités a priori, l’identification de l’ensemble de liens encombrés équivaut à trouver la solution d’un problème de maximisation de probabilité. Nous proposons une approche ascendante rapide appelée FBA pour trouver la solution à ce problème. Le FBA optimise la solution étape par étape de bas en haut. Nous prouvons que la solution du FBA est optimale.
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Haibo SU, Shijun LIN, Yong LI, Li SU, Depeng JIN, Lieguang ZENG, "A Fast Bottom-Up Approach to Identify the Congested Network Links" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 3, pp. 741-744, March 2010, doi: 10.1587/transcom.E93.B.741.
Abstract: In network tomography, most work to date is based on exploiting probe packet level correlations to infer the link loss rates and delay distributions. Some other work focuses on identifying the congested links using uncorrelated end-to-end measurements and link prior probability of being congested. In their work, the prior probabilities are identified by the matrix inversion with a number of measurement snapshots, and the algorithm to find the congested links is heuristic and not optimal. In this letter, we present a new estimator for the prior probabilities that is computationally simple, being an explicit function of the measurement snapshots. With these prior probabilities, the identification of the congested link set is equivalent to finding the solution for a probability maximization problem. We propose a fast bottom-up approach named FBA to find the solution for this problem. The FBA optimizes the solution step by step from the bottom up. We prove that the solution by the FBA is optimal.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.741/_p
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@ARTICLE{e93-b_3_741,
author={Haibo SU, Shijun LIN, Yong LI, Li SU, Depeng JIN, Lieguang ZENG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Fast Bottom-Up Approach to Identify the Congested Network Links},
year={2010},
volume={E93-B},
number={3},
pages={741-744},
abstract={In network tomography, most work to date is based on exploiting probe packet level correlations to infer the link loss rates and delay distributions. Some other work focuses on identifying the congested links using uncorrelated end-to-end measurements and link prior probability of being congested. In their work, the prior probabilities are identified by the matrix inversion with a number of measurement snapshots, and the algorithm to find the congested links is heuristic and not optimal. In this letter, we present a new estimator for the prior probabilities that is computationally simple, being an explicit function of the measurement snapshots. With these prior probabilities, the identification of the congested link set is equivalent to finding the solution for a probability maximization problem. We propose a fast bottom-up approach named FBA to find the solution for this problem. The FBA optimizes the solution step by step from the bottom up. We prove that the solution by the FBA is optimal.},
keywords={},
doi={10.1587/transcom.E93.B.741},
ISSN={1745-1345},
month={March},}
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TY - JOUR
TI - A Fast Bottom-Up Approach to Identify the Congested Network Links
T2 - IEICE TRANSACTIONS on Communications
SP - 741
EP - 744
AU - Haibo SU
AU - Shijun LIN
AU - Yong LI
AU - Li SU
AU - Depeng JIN
AU - Lieguang ZENG
PY - 2010
DO - 10.1587/transcom.E93.B.741
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2010
AB - In network tomography, most work to date is based on exploiting probe packet level correlations to infer the link loss rates and delay distributions. Some other work focuses on identifying the congested links using uncorrelated end-to-end measurements and link prior probability of being congested. In their work, the prior probabilities are identified by the matrix inversion with a number of measurement snapshots, and the algorithm to find the congested links is heuristic and not optimal. In this letter, we present a new estimator for the prior probabilities that is computationally simple, being an explicit function of the measurement snapshots. With these prior probabilities, the identification of the congested link set is equivalent to finding the solution for a probability maximization problem. We propose a fast bottom-up approach named FBA to find the solution for this problem. The FBA optimizes the solution step by step from the bottom up. We prove that the solution by the FBA is optimal.
ER -