Fast Automated Estimation of Variance in Discrete Quantitative Stochastic Simulation

Quantitative stochastic simulation is an important tool in assessing the performance of complex dynamic systems such as modern communication networks. Because of the proliferation of computers and devices that use and rely on networks such as the internet, assessing the performance of these networks is important to ensure future reliability and service. The current methodology for the analysis of output data from stochastic simulation is focused mainly on the estimation of means. Research on variance estimation focuses mainly on the estimation of the variance of the mean, as this is used to construct confidence intervals for the estimated mean values. To date, there has been little research on the estimation of variance of auto correlated data, such as those collected during steady-state stochastic simulation. This research investigates different methodologies for estimation of variance of terminating and steady-state simulation. Results from the research are implemented in the simulation tool Akaroa2.