Analysis of an importance sampling estimator for tandem queues
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The balanced likelihood ratio method for estimating performance measures of highly reliable systems
Proceedings of the 30th conference on Winter simulation
Efficient simulation of a tandem Jackson network
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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Over the last decade, importance sampling has been a popular technique for the efficient estimation of rare event probabilities. This paper presents an approach for applying balanced likelihood ratio importance sampling to the problem of quantifying the probability that the content of the second buffer in a two node tandem Jackson network reaches some high level before it becomes empty. Heuristic importance sampling distributions are derived that can be used to estimate this overflow probability in cases where the first buffer capacity is finite or infinite. The proposed importance sampling distributions differ from previous balanced likelihood ratio methods in that they are specified as functions of the contents of the buffers. Empirical results indicate that the relative errors of these importance sampling estimators is bounded independent of the buffer size when the second server is the bottleneck and is bounded linearly in the buffer size otherwise.