Experimental evaluation of confidence interval procedures in sequential steady-state simulation

Sequential analysis of simulation output is generally accepted as the most efficient way for securing representativeness of samples of collected observations. In this scenario a simulation experiment is stopped when the relative precision of estimates, defined as the relative width of confidence intervals at an assumed confidence level, reaches the required level. This paper deals with the statistical correctness of the methods proposed for estimating confidence intervals for mean values in sequential steady-state stochastic simulation. We formulate basic rules that should be followed in proper experimental analysis of coverage of different steadystate interval estimators. Our main argument is that such analysis should be done sequentially. The numerical results of our preliminary coverage analysis of the method of Spectral Analysis (SA/HW) and Nonoverlapping Batch Means are presented, and compared with those obtained by traditional, non-sequential approaches

Experimental evaluation of confidence interval procedures in sequential steady-state simulation

Sequential analysis of simulation output is generally accepted as the most efficient way for securing representativeness of samples of collected observations. In this scenario a simulation experiment is stopped when the relative precision of estimates, defined as the relative width of confidence intervals at an assumed confidence level, reaches the required level. This paper deals with the statistical correctness of the methods proposed for estimating confidence intervals for mean values in sequential steady-state stochastic simulation. We fonnulate basic rules that should be followed in proper experimental analysis of coverage of different steadystate interval estimators. Our main argument is that such analysis should be done sequentially. The numerical results of our preliminary coverage analysis of the method of Spectral Analysis (SNHW) and Nonoverlapping Batch Means are presented, and compared with those obtained by traditional, non-sequential approaches.

Wavelength and time division multiplexing with lightpath trespassing for all-optical star local area networks

Many medium access control protocols have been proposed for optical wavelength division multiplexing local area networks with a star topology. These protocols range from those based on the concept of fixed-assignment of communication subchannels, such as TDMA (Time Division Multiple Access); reservation of communication subchannels, such as DAS (Dynamic Allocation Scheme); or random-access to communication subchannels, such as DT-WDMA (Dynamic Time-Wavelength Division Multiple Access). In addition various hybrid protocols have been considered, for example, protocols incorporating both fixed-assignment and reservation rules, such as HTDM (Hybrid TDM). This thesis is on a novel hybrid protocol of fixed-assignment and random-access called "WTDMA with lightpath trespassing". This protocol combines the most desirable aspects of fixed-assignment and random-access protocols, while limiting their drawbacks. The performance of different versions of the protocol are analysed both mathematically and by stochastic simulation. The obtained results justify the introduction of the WTDMA with trespassing protocol, and indicate the situations where its use is advantageous

On automated sequential steady-state simulation.

The credibility of the final results from stochastic simulation has had limited discussion in the simulation literature so far. However, it is important that the final results from any simulations be credible. To achieve this, validation, which determines whether the conceptual simulation model is an accurate representation of the system under study, has to be done carefully. Additionally, a proper statistical analysis of simulation output data, including a confidence interval or other assessment of statistical errors, has to be conducted before any valid inferences or conclusions about the performance of simulated dynamic systems, such as for example telecommunication networks, are made. There are many other issues, such as choice of a good pseudo-random number generator, elimination of initialisation bias in steady-state simulations, and consideration of auto correlations in collected observations, which have to be appropriately addressed for the final results to be credible. However, many of these issues are not trivial, particularly for simulation users who may not be experts in these areas. As a consequence, a fully-automated simulation package, which can control all important aspects of stochastic simulation, is needed. This dissertation focuses on the following contributions to such a package for steady-state simulation: properties of confidence intervals (CIs) used in coverage analysis, heuristic rules for improving the coverage of the final CIs in practical applications, automated sequential analysis of mean values by the method of regenerative cycles, automatic detection of the initial transient period for steady-state quantile estimation, and sequential steady-state quantile estimation with the automated detection of the length of initial transient period. One difficulty in obtaining precise estimates of a system using stochastic simulation can be the cost of the computing time needed to collect the large amount of output data required. Indeed there are situations, such as estimation of rare events, where, even assuming an appropriate statistical analysis procedure is available, the cost of collecting the number of observations needed by the analysis procedure can be prohibitively large. Fortunately, inexpensive computer network resources enable computationally intensive simulations by allowing us to run parallel and distributed simulations. Therefore, where possible, we extend the contributions to the distributed stochastic simulation scenario known as the Multiple Replications In Parallel (MRIP), in which multiple processors run their own independent replications of the simulated system but cooperate with central analysers that collect data to estimate the final results