The search functionality is under construction.

The search functionality is under construction.

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.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E93-B No.3 pp.741-744

- Publication Date
- 2010/03/01

- Publicized

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.E93.B.741

- Type of Manuscript
- LETTER

- Category
- Network Management/Operation

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.

Copy

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

Copy

@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},}

Copy

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 -