ted values and graph (b) gives the corresponding relative error of the 8 Dimensioning for Multi-lub RAN Scenario calculated overbooking factor compared to the simulated results. Both graphs of Figure 8.21 demonstrate that the proposed analytical approach is able to provide fairly precise estimations on the optimum overbooking factor for the backbone link in the IP based multi-lub UTRAN. Overbooking: NRT traffic with last mile utilization = 30% Overbooking: NRT traffic with last mile utilization = 30% M/G/R/N-PS Simulation Number of Node Bs Number of Node Bs (a) overbooking factor (b) relative error Figure 8.21: Validating overbooking factor vs. the number of Node Bs (web traffic) Figure 8.22 presents the impact of different last mile utilizations on the obtained optimum overbooking factor. In the given example, the simulation setup is same as the one above, only that the number of Node Bs in the RAN is fixed to 4 and the offered traffic per Node B (i.e. last mile utilization) is varying by means of changing the maximum allowed flows per service class. 4 Node Bs, NRT traffic with different last mile utilization 4 Node Bs, NRT traffic with different last mile utilization M/G/R/N-PS Simulation last mile utilization last mile utilization (a) overbooking factor (b) relative error Figure 8.22: Overbooking factor vs. last mile utilizations (4 Node Bs) - web traffic Figure 8.22(a) shows that the overbooking factor decreases when the utilization of each last mile link gets higher. This rule is also consistent with the one observed from Appendix A.19 for investigating the impact of different last mile utilizations in the ATM-based UTRAN. Again Figure 8.22 validates the accuracy of the analytical approach for predicting optimum overbooking factors. 8.5 Overbooking of the lub Backbone Link Figure 8.23 presents a mixed traffic scenario. In this example, the reserved bandwidth by the RT traffic has been derived separately and the RT traffic has higher priority. So the task below is mainly to dimension a proper bandwidth for tr