| On the propagation of long-range dependence in the Internet |
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Applications, Technologies, Architectures, and Protocols for Computer Communication
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Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
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Stockholm, Sweden
Pages: 243 - 254
Year of Publication: 2000
ISBN:1-58113-223-9
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Authors
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A. Veres
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Traffic Laboratory, Ericsson Research, H-1037, Laborc u. 1., Budapest, Hungary
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Kenesi S. Molnár
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HSN Laboratory, Dept. of Telecomm and Telematics, Budapest University of Technology and Economics, H-1117, Pázmány P. 1/D, Budapest, Hungary
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G. Vattay
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Department of Physics of Complex Systems, Eötvös University, H-1518 Pf. 32, Budapest, Hungary and COMET group, Columbia University, New York, NY
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Downloads (6 Weeks): 7, Downloads (12 Months): 36, Citation Count: 8
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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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P. Abry and D. Veitch. Wavelet analysis of long-range-dependent traffic. IEEE/ACM Transactions on Networking, 44(1):2-15, 1998.
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Mark W. Garrett , Walter Willinger, Analysis, modeling and generation of self-similar VBR video traffic, Proceedings of the conference on Communications architectures, protocols and applications, p.269-280, August 31-September 02, 1994, London, United Kingdom
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Will E. Leland , Murad S. Taqqu , Walter Willinger , Daniel V. Wilson, On the self-similar nature of Ethernet traffic, Conference proceedings on Communications architectures, protocols and applications, p.183-193, September 13-17, 1993, San Francisco, California, United States
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S. Molnar, T. D. Dang, and A. Vid acs. Heavy tailedness, long-range dependence and self-similarity in data traffic. In Proc. 7th International Conference on Telecommunication Systems Modeling and Analysis, Nashville, Tennessee, USA, March 1999.
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S. Molnar and A. Vid acs. On modeling and shaping self-similar ATM traffic. In Proc. 15th Int. Teletraffic Congress, Washington D.C., 1997.
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I. Norros. A storage model with self-similar input. Queuing Systems, 16:387-396, 1994.
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K. Park, G. Kim,andM.Crovella. On the effect of traffic self-similarity onnetwork performance. In In Proceedings of the SPIE International Conference on Performance and Control of Network Systems, pages 296-310, November 1997.
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M. S. Taqqu, V. Teverovsky, and W. Willinger. Estimators for long-range dependence: an empirical study. Fractals, 3(4):785-788, 1995.
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A. Veres and M. Boda. The chaotic nature of TCP congestion control. In Proc. IEEE INFOCOM, Tel Aviv, Israel, April 2000.
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W. Willinger, M. S. Taqqu, and A. Erramilli. A bibliographical guide to self-similar traffic and performance modeling for modern high-speed networks, In Stochastic networks: Theory and applications, Ed:F. P. Kelly, S. Zachary and I. Ziedins. Clarendon Press, Oxford, 1996.
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CITED BY 8
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H. Sivakumar , S. Bailey , R. L. Grossman, PSockets: the case for application-level network striping for data intensive applications using high speed wide area networks, Proceedings of the 2000 ACM/IEEE conference on Supercomputing (CDROM), p.37-es, November 04-10, 2000, Dallas, Texas, United States
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INDEX TERMS
Primary Classification:
C.
Computer Systems Organization
C.2
COMPUTER-COMMUNICATION NETWORKS
C.2.5
Local and Wide-Area Networks
Subjects:
Internet (e.g., TCP/IP)
Additional Classification:
C.
Computer Systems Organization
C.2
COMPUTER-COMMUNICATION NETWORKS
C.2.2
Network Protocols
Nouns:
TCP/IP
G.
Mathematics of Computing
G.1
NUMERICAL ANALYSIS
G.1.3
Numerical Linear Algebra
Subjects:
Linear systems (direct and iterative methods)
I.
Computing Methodologies
I.2
ARTIFICIAL INTELLIGENCE
I.2.8
Problem Solving, Control Methods, and Search
Subjects:
Control theory
General Terms:
Design,
Management,
Measurement,
Performance,
Standardization,
Theory
Keywords:
TCP adaptivity,
TCP congestion control,
long-range dependence,
self-similarity
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