 Based on the above delay analysis on the lub, it can be concluded that the lub transport delay (i.e. FP PDU delay) can be approximated by the AAL2 queuing delay. So for the sake of the lub dimensioning to decide an optimum lub bandwidth with the minimum transport cost for satisfying lub transport delay requirements, the estimation of the AAL2 queuing delay is important. If the AAL2 queuing delay can be estimated analytically given certain lub link bandwidth, then the optimum lub capacity which meets the defined lub transport delay requirements can be obtained numerically. In queuing theory, the queuing delay is determined by the queuing models which consist of arrival traffic process, queue management, packet scheduling and service process, etc. So for setting up the analytical dimensioning approach, it is essential to construct appropriate queuing models to analytically calculate the AAL2 queuing delay and hence apply them to dimension the lub capacity properly to meet required lub delay constraints. Naturally, the construction of queuing models depends on the structure of the system models. Generally, the system model of the AAL2 layer may consist of a number of AAL2 queues for serving different traffic types with different priorities. Depending on different QoS structures and packet scheduling on the AAL2 layer, the system models are configured differently. In the scope of this thesis, two system models are considered. In the ATM-based transport network two QoS classes are regarded on the AAL2 packet level: Real Time (RT) traffic and Non Real Time (NRT) traffic. The applied packet scheduling between the two traffic classes is strict priority, where the RT traffic is given higher priority while NRT traffic uses any bandwidth left by the RT traffic. Thus, according to different traffic scenarios two system models are structured as illustrated in Figure 6.43: (a) Single-service system: there is only one traffic type in the system; (b) Multi-service system: there are both real time traffic and non real time traff