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On the propagation of long-range dependence in the Internet
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Source Applications, Technologies, Architectures, and Protocols for Computer Communication archive
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication table of contents
Stockholm, Sweden
Pages: 243 - 254  
Year of Publication: 2000
ISBN:1-58113-223-9
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Authors
A. Veres  Traffic Laboratory, Ericsson Research, H-1037, Laborc u. 1., Budapest, Hungary
Kenesi S. Molnár  HSN Laboratory, Dept. of Telecomm and Telematics, Budapest University of Technology and Economics, H-1117, Pázmány P. 1/D, Budapest, Hungary
G. Vattay  Department of Physics of Complex Systems, Eötvös University, H-1518 Pf. 32, Budapest, Hungary and COMET group, Columbia University, New York, NY
Sponsor
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper analyzes how TCP congestion control can propagate self-similarity between distant areas of the Internet. This property of TCP is due to its congestion control algorithm, which adapts to self-similar fluctuations on several timescales. The mechanisms and limitations of this propagation are investigated, and it is demonstrated that if a TCP connection shares a bottleneck link with a self-similar background traffic flow, it propagates the correlation structure of the background traffic flow above a characteristic timescale. The cut-off timescale depends on the end-to-end path properties, e.g., round-trip time and average window size. It is also demonstrated that even short TCP connections can propagate long-range correlations effectively. Our analysis reveals that if congestion periods in a connection's hops are long-range dependent, then the end-user perceived end-to-end traffic is also long-range dependent and it is characterized by the largest Hurst exponent. Furthermore, it is shown that self-similarity of one TCP stream can be passed on to other TCP streams that it is multiplexed with. These mechanisms complement the widespread scaling phenomena reported in a number of recent papers. Our arguments are supported with a combination of analytic techniques, simulations and statistical analyses of real Internet traffic measurements.


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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Collaborative Colleagues:
A. Veres: colleagues
Kenesi S. Molnár: colleagues
G. Vattay: colleagues

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