Self-Similarity in the Wide Sense for Information Flows With a Random Load Free on Distribution

For description of dynamics of changes random loads of information flows we examine the stochastic model of Double Stochastic Poisson process which manages points of changes the random loads. A special case of a discrete distribution for the random intensity provides the following covariance property to the corresponding Double Stochastic Poisson subordinator for a sequence of the random loads. Such covariance exactly coincides with the covariance of the fractional Ornstein-Uhlenbeck process. Applying the Lamperti transform we obtain a self-similar random process with continuous time, stationary in the wide sense increments, and one dimensional distributions scaling the distribution of a term of the the initial subordinated sequence of the random loads. The Central Limit Theorem for vectors allows us to obtain in a limit, in the sense of convergence of finite dimensional distributions, the fractional Gaussian Brownian motion and the fractional Ornstein-Uhlenbeck process.