Statistical Analysis of Next Generation Network Traffics Based on Wavelets and Transformation ON/(ON+OFF)

The significant increase of trunk channel bandwidth makes much easier to integrate different types of traffics on the tier links without activating high processing power consuming QoS (Quality of Service) mechanisms in the intermediate nodes. Self-similarity, long range dependence and fractal characteristics of packet flows are strongly influenced by the QoS parameters in congested network environment. Several models are proposed for the qualitative and quantitative evaluation of physical phenomenon supervened on different OSI layers at the routers and switches. Most of these claims relatively long traces for evaluating both scale independence and fractal characteristics. The highlights of common usage of wavelet and ON/(ON+OFF) transformations in network traffic analysis are evaluated in this chapter. We take into consideration the channel load and the channel intensity as complex time series for evaluation the statistical characteristics of changes in time of the flows nature in packet switched networks. UDP and TCP traffics in tier and LAN networks are considered and statistically analyzed based on MRA (Multi Resolution Analysis) wavelets method. A fast detection algorithm of data and real time traffic burstiness is presented for a QoS based packet switched network environment with congestion.