CU-Boulder ECEN 5032 - The Chaotic Nature of TCP Congestion Control (9 pages)

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The Chaotic Nature of TCP Congestion Control



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The Chaotic Nature of TCP Congestion Control

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Pages:
9
School:
University of Colorado at Boulder
Course:
Ecen 5032 - Special Topics
Special Topics Documents

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The Chaotic Nature of TCP Congestion Control And Veres Ericsson Traffic Analysis and Network Performance Laboratory Budapest Hungary e mail Andras Veres 1t eth ericsson se Mikl6s Boda Ericsson Research Stockholm Sweden e mail Miklos Boda era t ericsson se Abstract In this paper we demonstrate how TCP congestion control can show chaotic behavior We demonstrate the major features of chaotic systems in TCPlIP networks with examples These features include unpredictability extreme sensitivity to initial conditions and odd periodicity Previous work has shown the fractal nature of aggregate TCPAP traffic and one explanation to this phenomenon was that traffic can be approximated by a large number of ON OFF sourceswhere the random ON and or OFF periods are of length described by a heavy tailed distribution In this paper we show that this argument is not necessary to explain selfsimilarity neither randomness is required Rather TCP itself as a deterministic process creates chaos which generates self similarity This property is inherent in todays TCPlIP networks and it is independent of higher layer applications or protocols The two causes heavy tailed ONlOFF and chaotic TCP together contribute to the phenomena called fractal nature of Internet traffic Keywords TCP congestion control fractal traffic chaotic models I INTRODUCTION Traffic models used to model current Internet traffic can be categorized into two major groups linWsource and network level models Link level models fit statistical models to measurements of traffic on network links or traffic sources for example a WWW server Recently the largest contribution in this area was the exploration of fractal and long range dependent property of traffic namely that the second order statistics of traffic volumes observed at different scales does not change This resulted as a revolution in performance modeling and questioned previous models based on Markovian behavior see Paxson and Floyd 121 We mention two major publications



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