Complex Dynamics in Communication Networks (Understanding by Ljupco Kocarev, Gabor Vattay

By Ljupco Kocarev, Gabor Vattay

Desktop and communique networks are between society's most crucial infrastructures. the web, particularly, is a big worldwide community of networks with principal keep watch over or management. it's a paradigm of a fancy procedure, the place complexity may perhaps come up from various resources: topological constitution, community evolution, connection and node range, and /or dynamical evolution. this can be the 1st publication solely dedicated to the recent and rising box of nonlinear dynamics of TCP/IP networks. It addresses either scientists and engineers operating within the common box of conversation networks.

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Extra resources for Complex Dynamics in Communication Networks (Understanding Complex Systems)

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Accordingly we do not know how generic its appearance is. For example, it has been argued in Ref. [7] that in the chaotic examples shown in Ref. [4] the packet loss probability is considerably higher than 1%. It has been shown [7, 8, 3] that in this case the exponential backoff mechanism plays an important role and can be responsible for the complex congestion window dynamics. In this contribution we show that chaos is not a consequence of high network congestion or loss probability and that TCPs operating in congestion avoidance mode, never entering into a backoff state show chaos.

In reality, shown in Fig. 2(a) it can be observed that the congestion window corresponding to the largest round trip time is significantly larger than the others. This TCP obtains unfairly higher throughput than the other two. The packet loss rate of the preferred TCP (∼ 5 · 10−5 ) is also an order of magnitude less than that of the suppressed ones (∼ 7·10−4 ). The “winner” TCP behaves seemingly periodically, while the “losers” seem to be erratic. Obviously, there is something here which is missed completely by the stochastic model.

Virtual InterNetwork Testbed: Status and research agenda. Technical Report 98-678, University of Southern California, July 1998. 3. P. Barford and M. Crovella. Generating representative Web workloads for network and server performance evaluation. Performance Evaluation Review, 26(1):151–160, 1998. 4. K. J. Falconer. Fractal Geometry: Mathematical Foundations and Applications. Wiley& Sons, New York, 1990. 5. K. Fall and S. Floyd. Simulation-based comparisons of Tahoe, Reno, and SACK TCP. Computer Communication Review, 26(3):5–21, 1996.

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