Performance Analysis for E-Business: Impact of Long Range Dependence
Electronic Commerce Research
Data-unit-size distribution model when message segmentations occur
Performance Evaluation
Stochastic spectral density analysis on network traffic characterization
ICDCN'06 Proceedings of the 8th international conference on Distributed Computing and Networking
Design of a bandwidth-on-demand (BoD) protocol for satellite networks modelled as time-delay systems
Automatica (Journal of IFAC)
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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 IP traffic based on 90-minute traces taken from 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 packet stream 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.