Modeling randomness in network traffic

A continuous challenge in the field of network traffic modeling is to map recorded traffic onto parameters of random processes, in order to enable simulations of the respective traffic. A key element thereof is a convenient model which is simple, yet, captures the most relevant statistics. This work aims to find such a model which, more precisely, enables the generation of multiple random processes with arbitrary but jointly characterized distributions, auto-correlation functions and cross-correlations. Hence, we present the definition of a novel class of models, the derivation of a respective closed-form analytical representation and its application on real network traffic. Our modeling approach comprises: (i) generating statistical dependent Gaussian random processes, (ii) introducing auto-correlation to each process with a linear filter and, (iii) transforming them sample-wise by real-valued polynomial functions in order to shape their distributions. This particular structure allows to split the parameter fitting problem into three independent parts, each of which solvable by standard methods. Therefore, it is simple and straightforward to fit the model to measurement data.