used for the case when the desired end-to-end delay factor is much larger than 1 corresponding to a relatively low application QoS requirement. In the presented simulation example, the target end-to-end delay factor farget is set to 2. Thus, the expected end-to-end transfer delay for a page size of 50 kbyte T = Tmin frarger=9.1 = seconds, which is the target delay. The validation of the applicability of the approach (2) for dimensioning follows the same procedure as for validating the approach (1), only that the assignment of the target delay factors for the individual links is according to the approach (2): the target delay factor for the mile link is set to ftarget and the one for the backbone link is set to 1.1 in this example. Figure A.31(a) shows the average end-to-end delays Tunder different offered traffic load per last mile link. Every point of the curve represents an average end-to-end delay for a given offered traffic load. In the graph, the simulated values are compared to the target delay. Figure A.31(b) evaluates the deviation of the simulated delays to the expected end-to-end delay. As can be seen clearly from the graph (a), the average end-to-end delays obtained from the simulations fit quite well with the target end-to-end delays. The observed gap of the simulated delays with the expected end-to-end transfer delay is fairly small. This proves that the proposed approach (2) can provide sufficiently good QoS guarantee for large delay factors for the elastic traffic flows. Approach (2) desired E2E delay factor = 2 Approach (2) - desired E2E delay factor = 2 Target delay simulated delay offered traffic load on the last mile link (kbps) offered traffic load on the last mile link (kbps) (a) End-to-end delays (b) relative error Figure A.31: Validating Approach (2): E2E delays over different offered last mile loads The calculated bandwidths of the last mile link for different offered traffic load is given in Table A.9, as well as the corresponding achieved overbooking factor on the backbone link. A.19 Inv