Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 wide-area traces, investigating a number of wide-area TCP arrival processes (session and connection arrivals, FTP data connection arrivals within FTP sessions, and TELNET packet arrivals) to determine the error introduced by modeling them using Poisson processes. We find that user-initiated TCP session arrivals, such as remotelogin and file-transfer, are well-modeled as Poisson processes with fixed hourly rates, but that other connection arrivals deviate considerably from Poisson; that modeling TELNET packet interarrivals as exponential grievously underestimates the burstiness of TELNET traffic, but using the empirical Tcplib [Danzig et al, 1992] interarrivals preserves burstiness over many time scales; and that FTP data connection arrivals within FTP sessions come bunched into “connection bursts,” the largest of which are so large that they completely dominate FTP data traffic. Finally, we offer some results regarding how our findings relate to the possible self-similarity of widearea traffic.

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“…the trademark of modern culture is science; if you can fake this, you’ve got it made ”- Mario Bunge The need for a special issue of Emergence on the question “What is complexity science? ” is disturbing on several levels. At one level, one could be forgiven for thinking that the voluminous literature generated in recent years on chaos and complexity theory must contain a clear exposition on the definition, mission, and scope of complexity science. That this exposition has not been forthcoming, or is the subject of controversy, is disconcerting. On another level, the inability to clearly differentiate science from pseudoscience in complexity studies is also problematic. Allowing pseudo-science to penetrate a field of study lowers the credibility of that field with mainstream scientists and hinders the flow of resources for future development. It is my contention that much of the work in complexity theory has indeed been pseudo-science, that is, many writers in this field have used the symbols and methods of complexity science (either erroneously or deliberately) to give the illusion of science even though they lack supporting evidence and plausibility (Shermer, 1997). This proliferation of pseudo-science has, in turn, obscured the meaning and agenda of the science of complexity. The purpose of this article is twofold: 1) to provide a working definition of complexity science and 2) to use this definition to differentiate complexity science from complexity