15. Modeling Network Traffic

Different models are used to describe different types of traffic. For example, voice traffic is commonly described using the on–off source or the Markov modulated Poisson process (MMPP). Studies suggest that traffic sources such as variable bit rate (VBR) video and Ethernet traffic are better represented by self-similar traffic models [2–7]. The important characteristics of a traffic source are its average data rate, burstiness, and correlation. The average data rate gives an indication of the expected traffic volume for a given period of time. Burstiness describes the tendency of traffic to occur in clusters. A traffic burst affects buffer occupancy and leads to network congestion and data loss. Data burstiness is manifested by the autocorrelation function which describes the relation between packet arrivals at different times. It was recently discovered that network traffic exhibits long-range dependence, i.e. the autocorrelation function approaches zero very slowly in comparison with the exponential decay characterizing short-range dependent traffic [2–7]. Long-range dependent traffic produces a wide range in traffic volume away from the average rate. This great variation in traffic flow also affects buffer occupancy and network congestion. In summary, high burstiness or long-term correlation leads to buffer overflow and network congestion. We begin by discussing the different models describing traffic time arrival statistics.