 end-to-end delay factors, and the expected end-to-end delays (target delays). In addition, graph (b) evaluates the deviation of the simulated delays and the expected end-to-end delay. The results prove that approach (3) is capable of providing QoS guarantee for different ranges of delay factors and thus can be applied generally for dimensioning networks for elastic traffic. Furthermore, from Table 8.1 which gives the dimensioned bandwidths out of simulations and the resultant overbooking factors, it can be seen that approach (3) additionally achieves an efficient overbooking on the backbone link. Approach (3) - Desired E2E delay factor > 1 Approach (3) - Desired E2E delay factor > 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 8.11: Validating Approach (3). 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 Table 8.1: Dimensioning results for different target delay factors with approach (3) 8.4 Dimensioning for Elastic Traffic in IP-Based UTRAN 8.3.4 Summary In section 8.3, the dimensioning for elastic traffic in an ATM based multi-lub radio access network was presented. The analytical methods, which are based on the M/G/R-PS model, are developed to estimate the application performance on an end-to-end basis, taking into account of the individual impact of the last mile link and the backbone link. Based on that, three dimensioning approaches are proposed to determine the necessary bandwidths for the last mile link and the backbone link for a desired end-to-end delay factor as the QoS requirement. The applicability of the proposed three approaches for network dimensioning is demonstrated through simulations. Overall, the simulation results comply with the theoretical expectations sufficiently well. That means, the suggested three dimensioning approaches can work quite well for assigning a