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Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes
1/<i>f</i> noise and statistically self-similar processes such as fractional Brownian motion (fBm) are vital for modeling numerous real-world phenomena, from network traffic to DNA to the stock market. Although several algorithms exist for synthesizing discrete-time samples of a 1/<i>f</i> process, these algorithms are <i>inexact</i>, meaning that ...
Toward an Improved Understanding of Network Traffic Dynamics
Since the discovery of long range dependence in Ethernet LAN traces there has been significant progress in developing appropriate mathematical and statistical techniques that provide a physical-based, networking-related understanding of the observed fractal-like or self-similar scaling behavior of measured data traffic over time scales ranging from ...