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.

With the advent of fast computer systems, scientists are now able to generate terabytes of simulation data. Unfortunately, the sheer size of these data sets has made efficient exploration of them impossible. To aid scientists in gleaning insight from their simulation data, we have developed an ad-hoc query infrastructure. Our system, called AQSim (short for Ad-hoc Queries for Simulation) reduces the data storage requirements and query access times in two stages. First, it creates and stores mathematical and statistical models of the data at multiple resolutions. Second, it evaluates queries on the models of the data instead of on the entire data set. In this paper, we present two simple but effective statistical modeling techniques for simulation data. Our first modeling technique computes the “true” (unbiased) mean of systematic partitions of the data. It makes no

We acknowledge financial support from, funded by the Netherlands Organisation for Scientific Research (NWO). We gratefully acknowledge useful comments by Jeroen van den Bergh, Patrick Criqui and Henri de Groot on an earlier version of this paper. Of course, the usual disclaimer applies.