Traffic streams, sources as well as aggregated traffic flows, often exhibit long-range-dependent (LRD) properties. This paper presents the theoretical foundations to justify that the behavior of traffic in a high-speed computer network can be modeled from a self-similar perspective by limiting its scope of analysis to the network layer, since the most relevant properties of self-similar processes are consistent for use in the formulation of traffic models when performing this specific task.

Abstract:A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long range dependence LRD is presented by means of the formulation of a model that considers the use of piecewise affine one-dimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst's exponents is proposed and the feasibility of their control from the parameters of the proposed model is shown.

In a previous paper it was proposed, and theoretically confirmed, that analysis of self-similar traffic flows with long-range dependence may be restricted to the network layer. In this paper this novel concept is applied to the study of traffic recorded in an IEEE 802.3u network environment with the aim of proving its validity as a simple and efficient tool for high speed computer network traffic flow analysis.

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