Statistical Characterization of Wide-Area Self-Similar Network TrafficReport
Background traffic models are fundamental to packet-level network simulation since the background traffic impacts packet drop rates, queuing delays, end-to-end delay variation, and also determines available network bandwidth. In this paper, we present a statistical characterization of wide-area traffic based on a week-long trace of packets exchanged between a large campus network, a state-wide educational network, and a large Internet service provider. The results of this analysis can be used to provide a basis for modeling background load in simulations of wide-area packet-switched networks such as the Internet, contribute to understanding the fractal behavior of wide-area network utilization, and provide a benchmark to evaluate the accuracy of existing traffic models. The key findings of our study include the following: (1) both the aggregate and its component substreams exhibit significant long-range dependencies in agreement with other recent traffic studies, (2) the empirical probability distributions of packet arrivals are log-normally distributed, (3) packet sizes exhibit only short-term correlations, and (4) the packet size distribution and correlation structure are independent from both network utilization and time of day.
All rights reserved (no additional license for public reuse)
Lucas, Matthew, Dallas Wrege, Bert Dempsey, and Alfred Weaver. "Statistical Characterization of Wide-Area Self-Similar Network Traffic." University of Virginia Dept. of Computer Science Tech Report (1996).
University of Virginia, Department of Computer Science