racy by considering the TCP slow start mechanism but still does not include the effect of varying RTT and the additional delays for TCP connection setup and release. From the above results, it can be concluded that the adjusted and extended M/G/R-PS model proposed in section 6.3.7.1 improves the accuracy of the calculation of the mean file transfer delay under different load levels as well as for different file sizes. It gives a precise estimation of the application performance and in turn should enhance the dimensioning results. File Transfer Delay over Load (5Kbyte) File Transfer Delay over Load (100Kbyte) M/G/R-PS M/G/R-PS Extended M/G/R-PS Extended M/G/R-PS Adjusted & Extended M/G/R-PS Adjusted & Extended M/G/R-PS Simulations (no CAC) Simulations (no CAC) Figure 6.25: Average file transfer delay over load: ftp scenario, RAB 128 kbps, no CAC, constant file size (5; 100 kbyte), lub link = 1 E1 line As mentioned in section 6.3.5, an important feature of the M/G/R-PS model is that the average sojourn time (average time in system) is insensitive to file size distributions. To validate this property, several file size distributions with the same mean value are investigated. In Figure 6.26, constant, exponential, hyper-exponential and heavy-tail Pareto file size distributions are compared, all with the same mean of 12 kbyte. 6 Dimensioning for Single ATM-Based lub Differrent File Size Distributions Adjusted & Extended M/G/R-PS constant distribution Pareto distribution exponential distribution Hyper-exp distribution (o 2) Figure 6.26: Different file size distributions: ftp scenario, RAB 128 kbps, no CAC, constant file size of12 kbyte, lub link = 1 E1 line In this figure, the parameter c2 represents the coefficient of variation (c is defined as the ratio of the standard deviation to the mean), which is used to estimate the level of burstiness of the traffic. The constant file size distribution represents the deterministic traffic, whose coefficient of variation is 0. For the exponential distribution C equals to 1, it 