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An independent-connection model for traffic matrices
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Source Internet Measurement Conference archive
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement table of contents
Rio de Janeriro, Brazil
SESSION: Traffic table of contents
Pages: 251 - 256  
Year of Publication: 2006
ISBN:1-59593-561-4
Authors
Vijayi Erramill  Boston University, Boston, MA
Mark Crovella  Boston University, Boston, MA
Nina Taft  Intel Research, Berkeley, CA
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

A common assumption made in traffic matrix (TM) modeling and estimation is independence of a packet's network ingress and egress. We argue that in real IP networks, this assumption should not and does not hold. The fact that most traffic consists of two-way exchanges of packets means that traffic streams flowing in opposite directions at any point in the network are not independent. In this paper we propose a model for traffic matrices based on independence of connections rather than packets. We argue that the independent-connection (IC) model is more intuitive, and has a more direct connection to underlying network phenomena than the gravity model. To validate the IC model, we show that it fits real data better than the gravity model and that it works well as a prior in the TM estimation problem. We study the model's parameters empirically and identify useful stability properties. This justifies the use of the simpler versions of the model for TM applications. To illustrate the utility of the model we focus on two such applications: synthetic TM generation and TM estimation. To the best of our knowledge this is the first traffic matrix model that incorporates properties of bidirectional traffic.


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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Internet Abilene Network http://www.internet2.org.
 
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Cao, J., Weil, S. V., and Yu, B. Time-Varying Network Tomography. Journal of the American Statistical Assoc. 2000 (2000).
 
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Erramilli, V., Crovella, M., and Taft, N. An Independent Connection model for Traffic Matrices. Tech. Rep. 2006-022, Computer Science Dept., Boston University, Boston, MA, USA, 2006.
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Medina, A., Salamatian, K., Taft, N., Matta, I., and Diot, C. A Two Step Statistical Approach for Inferring Network Traffic Demands. Tech. Rep. 2004-011, Boston University, Computer Science Department, March 2004.
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Collaborative Colleagues:
Vijayi Erramill: colleagues
Mark Crovella: colleagues
Nina Taft: colleagues