ved that the simulated delays are a little higher than the defined delay targets. This gap is due to that the proposed approach (1) neglects the correlations between concatenated links, as it dimensions each link independently according to the end-to-end delay factor assuming that this link is the bottleneck while neglecting the impact of the other link. Nevertheless, the presented results demonstrate that the proposed approach (1) can work sufficiently well for assigning appropriate bandwidths for the individual links in the network for satisfying small end-to-end delay factors (which are close to 1), despite the independence assumption. And secondly, the simulation results also verify that the extended M/G/R-PS model can predict the achievable application QoS accurately and thus can be used for dimensioning links and networks for elastic traffic for a guaranteed user application QoS. Approach (1) - Desired E2E delay factor close to 1 Approach (1) - Desired E2E delay factor close to 1 Target delay simulated delay Target end-to-end delay factor Target end-to-end delay factor (a) End-to-end delays (b) relative error Figure A.30: Validating Approach (1). E2E delays over different target delay factors Target end-to-end Last mile link Backbone link Overbooking factor delay factor ftarget bandwidth (kbps) bandwidth (kbps) for backbone link 1458.8 4817.7 1297.6 4416.8 1279.3 4289.6 1232.6 4229.9 Table A.8: Dimensioning results for different target delay factors with approach (1) In Table A.8, given the last mile link bandwidth and the backbone link bandwidth calculated from the approach (1), the overbooking factors of the backbone link are calculated with equation (8.1). It is observed that the obtained overbooking factors with the suggested Appendix dimensioning approach are larger than 1. That means the proposed dimensioning approach (1) is capable of attaining overbooking on the backbone link. II. Validation for Approach (2) The following simulation investigates the accuracy of the approach (2). The approach (2) is 