)]: (6.55) Then we match the above w to the mean waiting time of the approximated complementary distribution of the virtual waiting time, Wappr (x) in equation (6.49). Thus, the asymptotic constant a and a2need to satisfy: (6.56) a+a2=1 Then a1 and a2 can be calculated from (6.56). Here the sum of a and a2 should be equal to 1 (normalization of W(x) in equation (6.49)). 6.4 Dimensioning for Transport Network Performance (4) CalculateWapp(x): With the asymptotic decay rate S1 and S2 and the asymptotic constant a1 and A2, Wappr(x) given in equation (6.49) can be obtained. Furthermore, the waiting time distribution Fappr (x) (CDF) can also be derived from Wappr (x), i.e. = 6.4.3.3 MMPP(2)/D/1 - Non-Preemptive Priority This section deals with the system model case (b), where both RT traffic and NRT traffic are transmitted on the lub link, but the RT traffic is given priority over the NRT traffic. To estimate the distribution of the low priority queue, the considered queuing model is MMPP/D/1 with non-preemptive priority scheduling. An analytical approach for calculating the queuing delay distribution in an MMPP/G/1 non-preemptive queue is presented in [HZ05]. In this approach, the high priority traffic is modeled as a Poisson arrival process with rate Ah while the low priority traffic is modeled as an MMPP arrival process, which is characterized by a phase transition matrix R1 and arrival rate matrix . The high and low priority packets are assumed to have the same service time distribution with a mean service rate of . Figure 6.49 illustrates the possible delays a packet may experience at the AAL2 queue. First define a number of random variables corresponding to a tagged packet which arrives at the system in phase j. B denotes the full service time U denotes the partial service time V denotes the residual service time A denotes the number of packets queuing at the beginning of the service time during which the tagged packet arrives; Xj denotes the number of packets that arrived during the partial service time U. Qj d