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Fast accurate computation of large-scale IP traffic matrices from link loads
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Source Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems table of contents
San Diego, CA, USA
SESSION: Internet traffic engineering table of contents
Pages: 206 - 217  
Year of Publication: 2003
ISBN:1-58113-664-1
Also published in ...
Authors
Yin Zhang  AT&T Labs, Shannon Laboratory, Florham Park, NJ
Matthew Roughan  AT&T Labs, Shannon Laboratory, Florham Park, NJ
Nick Duffield  AT&T Labs, Shannon Laboratory, Florham Park, NJ
Albert Greenberg  AT&T Labs, Shannon Laboratory, Florham Park, NJ
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  53

Collaborative Colleagues:
Yin Zhang: colleagues
Matthew Roughan: colleagues
Nick Duffield: colleagues
Albert Greenberg: colleagues