• The KIPS Transactions:PartA
• /
• v.9A no.4
• /
• pp.519-524
• /
• 2002

In this paper we study the qualify of sequential coverage analysis under a scenario of distributed stochastic simulation known as MRIP(Multiple Replications In Parallel) in terms of the confidence intervals of coverage and the speedup. The estimator based in the F-distribution was applied to the sequential coverage analysis of steady-state means. in simulations of the $M/M/1/{\infty},\;M/D/I/{\infty}\;and\;M/H_{2}/1/{\infty}$ queueing systems on a single processor and multiple processors. By using multiple processors under the MRIP scenario, the time for collecting many replications needed in sequential coverage analysis is reduced. One can also easily collect more replications by executing it in distributed computers or clusters linked by a local area network.

• Journal of the Korea Society for Simulation
• /
• v.13 no.2
• /
• pp.23-34
• /
• 2004

Regenerative simulation (RS) is a method of stochastic steady-state simulation in which output data are collected and analysed within regenerative cycles (RCs). Since data collected during consecutive RCs are independent and identically distributed, there is no problem with the initial transient period in simulated processes, which is a perennial issue of concern in all other types of steady-state simulation. In this paper, we address the issue of experimental analysis of the quality of sequential regenerative simulation in the sense of the coverage of the final confidence intervals of mean values. The ultimate purpose of this study is to determine the best version of RS to be implemented in Akaroa2 [1], a fully automated controller of distributed stochastic simulation in LAN environments.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.219-228
• /
• 2004

Confidence interval estimators for proportions using normal approximation have been commonly used for coverage analysis of simulation output even though alternative approximate estimators of confidence intervals for proportions were proposed. This is -because the normal approximation was easier to use in practice than the other approximate estimators. Computing technology has no problem with dealing these alternative estimators. Recently, one of the approximation methods for coverage analysis which is based on arcsin transformation has been used for estimating proportion and for controlling the required precision in [12]. In this paper, we compare three approximate interval estimators, based on a normal distribution approximation, an arcsin transformation and an F-distribution approximation, of a single proportion. Three estimators were applied to sequential coverage analysis of steady-state means, in simulations of the M/M/1/$\infty$ and W/D/l/$\infty$ queueing systems on a single processor and multiple processors.