In computer networks, self-similarity is a feature of network data transfer dynamics. When modeling network data dynamics the traditional time series models, such as an autoregressive moving average model are not appropriate. This is because these models only provide a finite number of parameters in the model and thus interaction in a finite time window, but the network data usually have a long-range dependent temporal structure. A self-similar process is one way of modeling network data dynamics with such a long range correlation. This article defines and describes network data transfer dynamics in the context of a self-similar process. Properties of the process are shown and methods are given for graphing and estimating parameters modeling the self-similarity of network data. Definition Suppose be a weakly stationary (2nd-order stationary) process with mean , variance , and autocorrelation function . Assume that the autocorrelation function has the form as , where and is a slowly varying function at infinity, that is for all . For example, and are slowly varying functions. Let , where , denote an aggregated point series over non-overlapping blocks of size , for each is a positive integer. Exactly self-similar process is called an exactly self-similar process if there exists a self-similar parameter such that has the same distribution as . An example of exactly self-similar process with is Fractional Gaussian Noise (FGN) with . Definition:Fractional Gaussian Noise (FGN) is called the Fractional Gaussian Noise, where is a Fractional Brownian motion. exactly second order self-similar process is called an exactly second order self-similar process if there exists a self-similar parameter such that has the same variance and autocorrelation as . asymptotic second order self-similar process is called an asymptotic second order self-similar process with self-similar parameter if as , Some relative situations of Self-Similar Processes Long-Range-Dependence(LRD) Suppose be a weakly stationary (2nd-order stationary) process with mean and variance . The Autocorrelation Function (ACF) of lag is given by Definition: A weakly stationary process is said to be "Long-Range-Dependence" if A process which satisfies as is said to have long-range dependence. The spectral density function of long-range dependence follows a power law near the origin. Equivalently to , has long-range dependence if the spectral density function of autocorrelation function, , has the form of as where , is slowly varying at 0. also see Slowly decaying variances When an autocorrelation function of a self-similar process satisfies as , that