Complex System

REALWORLD NETWORKS Applied Visualization of Scale-free Network Models With Consideration of Bandwidth and Error Correcting Technology For Quality of Service, Virus and Threat Assessment Simulation

Chris Weiss, 2002 chris@chaotics.net

REALWORLD NETWORKS Applied Visualization of Scale-free Network Models with Consideration of Bandwidth and Error Correcting Technology for Quality of Service,Virus and Threat Assessment Simulation Chris Weiss, 2002 chris@chaotics.net

The situation that most of traditional science is focusing on linear systems can be compared to the story of the person who looks for the lost car keys under a street lam because it is too dark to see anything at the place where the keys were lost. Gottfried Mayer-Kress University of Illinois at Urbana-ChampaignOverview Inexplicable compulsion The Chaotics Network was born from an inexplicable compulsion toward chaos. For a decade I have been involved with designing, managing, assessing risks, looking for vulnerabilities and supporting large networks of geographically distributed personal computers. During that time I have seen it all. The one common thread that ties all of my experiences together is thefact that these huge,

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homogeneous networks all display similar behaviors. That is not to say they are well behaved, far from it, they are a constant source of failures, crashes, infections, intrusions, upgrades and breakdowns. But the one common thread that they all display is a certain determinism and a tendency toward emergence. So why chaos? I have been a chaos theory hoppyist since Ibegan using FRACTINT in 1988 on my Leading Edge 386 with a math coprocessor and SVGA 256 color capabilities. From that time on I have read a great deal of the popular works from authors like Gleick and Waldrop, and some of the more detailed text from the authorities on chaos, fractals and complexity like Yorke, Abraham and Mandelbrot. This research begins with some existing and well known models ofexponential random graphs networks (Erdös-Rényi, 1959) that display the small world effect (Milgram, 1967). Also pertinent to this work will be the studies of systematically rewired random graphs with small world properties (Watts-Stogratz, 1999) and randomly rewired (Newman-Watts, 1999) small world random graph networks. These models, combined with more applied scale-free networks(Albert-Barabási, 1999) with consideration to realworld clustering (Adamic, 1999), theoretically can produced a new and more pragmatic network model from which to work.

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In a nutshell, the Chaotic Network was founded in 2001 by Christopher Weiss to study and promote the idea that technological, information-driven societies can and should better understand, manage and secure distributed computer networksthrough the properties of chaos theory.

Chris Weiss Oakton Virginia January 8, 2002

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Abstract

A brief history of distributed computing One of the earliest paradigms in computing was that of a powerful centrally located processor performing many tasks for many people. In this model, control over the computing landscape was at its greatest. But mainframe computing was expensive and wasgetting behind in the area of raw processing power. The idea of decentralized computing power, spread out over a multitude of smaller and less expensive processors, sprang to life from this growing dilemma in the 1980s. The mainframe processing of the 1960s gave way to file servers and then client/server architectures where personal computers accessed data and applications from smaller serversusing much of their own processing power for computations. These systems could then return data to a central location for management, sharing and further processing. The problem that this new paradigm created was one of decentralized control. Suddenly, over the period of less than a decade, the computing world had small mainframe-like systems sprouting like mushrooms in a dark closet when most...