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Generating traffic time series (traffic for short) is important in networking, e.g., simulating the Internet. In this aspect, it is desired to generate a time series according to a given correlation structure that may well reflect the statistics of real traffic. Recent research of traffic modeling exhibits that traffic is well modeled by a type of Gaussian process called the generalized Cauchy (GC) process indexed by two parameters that separately characterize the self-similarity (SS), which is local property described by fractal dimension D, and long-range dependence (LRD), which is a global feature that can be measured by the Hurst parameter H, instead of using the linear relationship D = 2 − H as that used in traditional traffic model with a single parameter such as fractional Gaussian noise (FGN). This paper presents a computational method to generate series based on the correlation form of GC process indexed by 2 parameters. Hence, the present model can be used to simulate realizations that flexibly capture the fractal phenomena of real traffic for both short-term lags and long-term lags

Generating Traffic Time Series Based on Generalized Cauchy Process