 distribution is because the arrival traffic is usually rather bursty, where the coefficient of variation is larger than 1 as shown in section 6.4.2.1. Hyper-exponential distributions using 2 phases can model this variation by introducing two Poisson arrival processes where one 6 Dimensioning for Single ATM-Based lub Poisson process models very small packet interarrival times and the other models the larger packet interarrival times. Moreover, the Hyper(2) model requires the minimum number of parameters, i.e. the mean arrival time and the variance of arrival times. It has a freedom of tuning the parameters given the mean and variance, with various methods. As in the arrival of the AAL2 CPS packets, there are bulk arrivals. Because in the Rel99 model, the arrival of each FP PDU will be converted into a stream of AAL2 CPS packets and they are put into the AAL2 buffer at the same time. That means, a number of AAL2 CPS packets from the same FP PDU are coming to the AAL2 buffer with "zero" interarrival time. In order to model the bulk arrival of AAL2 CPS packets, a parameter tuning algorithm is proposed in equation (6.61) to calculate the above three Hyper-exponential parameters (i.e. a1, A2, and p1). The calculation is based on the mean time between arrivals u and the variance of arrivals V. To model the bulk arrival, let (1/a1) be close to 0 (e.g. 1e-10). p = a/(1-2-a).u+b) (6.61) P2=1-p1 With this approach, the Hyper-exponential parameters from the observed mean time between arrivals and the variance of arrivals can be obtained. However it is known that the Hyper-exponential distribution does not have any correlation, i.e. there is no correlation between packet arrivals in the Hyper-exponential distribution; SO in order to capture the correlations another parameter 6 is needed, which is defined as the decaying rate of the auto- correlation function. The Autocorrelation Function (ACF) for a samples series X, (i =1, 2, 3....) is defined in equation (6.62), where k is the lag. (6.62) Usually for Long Term Dependence (