Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
WSC '95 Proceedings of the 27th conference on Winter simulation
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
The accuracy of a new confidence interval method
WSC '04 Proceedings of the 36th conference on Winter simulation
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
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Simulation output data analysis in performance evaluation studies of complex stochastic systems such as the Internet is typically limited to mean values, even though it provides very limited information about the analysed system's performance. Quantile analysis is not as common, even though it can provide much deeper insights into the system of interest. A set of quantiles can be used to approximate a cumulative distribution function, providing full information about a given performance characteristic of the simulated system. In this paper, we will present two new methods for estimating steady state quantiles and distribution functions. The quantiles are estimated using simulation output data from concurrently executed independent replications. They are calculated sequentially and on-line to guarantee that their final statistical errors do not exceed a permitted threshold.