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The chaotic nature of TCP congestion control

Authors: Andras Veres; Miklós Boda;

The chaotic nature of TCP congestion control

Abstract

In this paper we demonstrate how TCP congestion control can show chaotic behavior. We demonstrate the major features of chaotic systems in TCP/IP networks with examples. These features include unpredictability, extreme sensitivity to initial conditions and odd periodicity. Previous work has shown the fractal nature of aggregate TCP/IP traffic and one explanation to this phenomenon was that traffic can be approximated by a large number of ON/OFF sources where 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 self-similarity, neither is randomness is required. Rather, TCP itself as a deterministic process creates chaos, which generates self-similarity. This property is inherent in today's TCP/IP networks and it is independent of higher layer applications or protocols. The two causes, heavy-tailed ON/OFF and chaotic TCP together contribute to the phenomenon, called the fractal nature of Internet traffic.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
126
Top 10%
Top 1%
Top 1%
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