Towards An Explanatory Model for Network Traffic

Abstract

This work presents two explanatory mathematical models explaining how network traffic features that display Gaus-sian tendencies in single devices and small networks aggregate to alpha-stable processes in larger networks. The first model shows how self-similarity originates from an impulsive-noise-based representation of individual processes. A second model uses renewal processes to justify impulsive process aggregation to alpha-stable or Gaussian end states and permits estimating network traffic alpha-stable rates of convergence. We develop a model based on this first method to empirically validate this aggregation approach.