nificance of correlated random sequences. Moreover, with the LRE method the simulation run time can be controlled by comparing the estimated error from the simulation results with a predefined maximum error. In this thesis, the LRE method is used to investigate whether the configured simulation run time is long enough to provide reliable and stable statistical evaluations of the simulation results. The LRE method is one of the class in the Communication Network Class Library (CNCL) developed at the Chair of Communication Networks, RWTH Aachen. In this thesis, the Discrete Limited Relative Error (DLRE) is used, which is the LRE optimized for discrete, correlated sequences. The DLRE is used in this thesis to estimate the relative error d(x) for the queuing delay distribution function F(x), in order to investigate if the simulation run time is sufficient for providing reliable queuing delay distribution function. The queuing delay distribution is important for estimating the packet loss ratio. Figure A.40 gives an example of investigating the queuing delay distribution function of the voice traffic in the AAL2 RT buffer. In this example, there is only voice traffic transmitting on a 1.7 Mbps ATM lub link, with 72.8% link utilization. The simulation run time is 3600 seconds. The maximum allowed relative error is set to be 1%. It can be observed from Figure A.40 that the Appendix estimated relative error d(x) for the queuing delay distribution function F(x) has rather low relative errors (lower than 0.3%) for different queuing delay ranges. This example demonstrates that for 3600 seconds simulation run time, the obtained queuing delay distribution function is reliable with relative errors far below the maximum allowed relative error of 1%. CDF of voice queuing delay F(x) and rel. error from LRE d(x) 0.022 0.018 0.016 0.014 0.012 0.008 0.006 0.004 0.002 queuing delay (s) Figure A.40: LRE distribution function F(x) and relative error d(x) for queuing delays of circuit-switched voice: voice scenario, lub link = 1.7 Mbps 