The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fast accurate computation of large-scale IP traffic matrices from link loads
Abstract
A matrix giving the traffic volumes between origin and destination in a network has tremendously potential utility for network capacity planning and management. Unfortunately, traffic matrices are generally unavailable in large operational IP networks. On the other hand, link load measurements are readily available in IP networks. In this paper, we propose a new method for practical and rapid inference of traffic matrices in IP networks from link load measurements, augmented by readily available network and routing configuration information. We apply and validate the method by computing backbone-router to backbone-router traffic matrices on a large operational tier-1 IP network -- a problem an order of magnitude larger than any other comparable method has tackled. The results show that the method is remarkably fast and accurate, delivering the traffic matrix in under five seconds.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
Meng, X., Pappas, V., & Zhang, L. (2010) · 2010 Proceedings IEEE INFOCOM · doi:10.1109/infcom.2010.5461930 · Cited by 935
Structural analysis of network traffic flows
Lakhina, A., Papagiannaki, K., Crovella, M., Diot, C., Kolaczyk, E. D., & Taft, N. (2004) · Proceedings of the joint international conference on Measurement and modeling of computer systems · doi:10.1145/1005686.1005697 · Cited by 451
Sensitivity of PCA for traffic anomaly detection
Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007) · Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems · doi:10.1145/1254882.1254895 · Cited by 353
Metadata
- Authors
- Zhang, Y., Roughan, M., Duffield, N., & Greenberg, A.
- Venue
- Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
- Publisher
- ACM
- Published
- 2003-06-10
- Pages
- 206-217
- OpenAlex
- W1970701242
- Cited by
- 505
- References
- 1
- FWCI
- 18.32
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fast accurate computation of large-scale IP traffic matrices from link loads. https://app.cochat.ai/papers/10.1145/781027.781053
- The paper
- Zhang, Y., Roughan, M., Duffield, N., & Greenberg, A. (2003). Fast accurate computation of large-scale IP traffic matrices from link loads. Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems. https://doi.org/10.1145/781027.781053
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
