Statistical Characterization of Wide-Area IP Traffic

Background traac models are fundamental to packet-level network simulation since the background traac 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 IP traac based on 90-minute traces taken from a week-long trace of packets exchanged between a large campus network, a statewide 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 traac models. The key ndings of our study include the following: (1) both the aggregate packet stream and its component substreams exhibit significant long-range dependencies in agreement with other recent traac 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.