generates pure random traffic. The hyper-exponential distribution produces a bursty traffic with c2 >1, in given scenario c2=4. The Pareto distribution is a heavy tailed distribution, it is configured with c2=12. It can be seen that in the low load situations, the delay from all file size distributions are same. When the load goes higher, the transfer delays get increased in general as a result the lub link congestion. Furthermore it is found that constant, exponential and hyper- exponential file size distributions achieve almost similar application performance, which demonstrates the property of independency of the file size distributions to the average sojourn delays. However, Pareto file size distribution experiences significantly longer delays than other distributions when the load is above 50% of the link capacity. This negative effect of underestimating the transfer time for the Pareto distribution is caused by not taking the queuing delays in the buffer of the bottleneck link into account for the experienced RTT. This is because that in addition to its transmission time queuing delays over the link also have considerable impact to the round trip times. For a more bursty source traffic, this impact becomes more significant. As with heavy-tail Pareto distribution, the aggregated traffic on the lub link is much more bursty than other file size distributions, as a result the degradation of the queuing performance under Pareto distribution is more serious. In order to improve the accuracy of estimations for the RTT in the case of a highly bursty source traffic, it is proposed by to calculate an estimated round trip time which also includes the queuing delays in the buffer of the bottleneck link. It is assumed that in times where more than R flows are active (i.e. the sum of the peak rates exceeds the link capacity C), the buffer fills up quickly and the 6.3 Dimensioning for Elastic Traffic queue is mostly at its limit. This corresponds to serving B packets of length Lp with capacity C. In times of n < R, it is 