Performance Bounds for a Cascade based Multifractal Traffic Model with Generalized Multiplier Distributions

Abstract

In this paper we propose a multifractal traffic model that is based on a multiplicative cascade with generalized multiplier distributions (CGMD). The multipliers are determined through their probability densities estimated from real network traffic flows by using Kernel and Acceptance/Rejection methods. Statistical analysis and queueing behavior studies were carried out for the model validation in comparison to other multiplicative cascade based models. In order to build an efficient estimation method of performance bounds for network traffic flows that takes account of multifractal characteristics, we derive the effective bandwidth for the CGMD model as well as its Hurst parameter. The proposed performance bounds are computed by relating the CGMD based effective bandwidth to statistical network calculus concepts. Our performance bound estimation approach is evaluated through simulations with Internet and Ethernet traffic traces, verifying its efficiency in describing the byte loss probability and mean buffer occupation.