Analysis and modelling of ATM (AAL5) traffic traces

Abstract

We present an analysis of Asynchronous Transfer Mode {ATM) traffic data collected from the London MAN {Metropolitan Area Network). The traffic is found to have different characteristics depending on its intensity. The correlations between inter-arrival times of sessions for traffic from heavily utilised sites is insignificant at the 5% significance level and a Poisson Point process is thus appropriate as a model. Traffic from low utilisation sites, on the other hand, shows long-memory characteristics with correlation at lag 40 and more. This traffic is modelled firstly using an autoregressive process and then the well known Markov Modulated Poisson Process {MMPP). The burst size distribution for both correlated and uncorrelated traffic shows distinct peaks which corresponds to the fragmentation as data travels through networks with different Maximum Transport Units {MTUs).