Efficient tail estimation for sums of correlated lognormals
Proceedings of the 40th Conference on Winter Simulation
On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Rare event simulation techniques
Proceedings of the Winter Simulation Conference
Importance sampling for stochastic recurrence equations with heavy tailed increments
Proceedings of the Winter Simulation Conference
Efficient importance sampling schemes for a feed-forward network
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Hi-index | 0.00 |
We develop a strongly efficient rare-event simulation algorithm for computing the tail of the steady-state waiting time in a single server queue with regularly varying service times. Our algorithm is based on a state-dependent importance sampling strategy that is constructed so as to be straightforward to implement. The construction of the algorithm and its asymptotic optimality rely on a Lyapunov-type inequality that is used to bound the second moment of the estimator. The solution to the Lyapunov inequality is constructed using fluid heuristics. Our approach takes advantage of the regenerative ratio formula for the steady-state distribution--and does not use the first passage time representation that is particular to the delay in the G/G/1 queue. Hence, the strategy has the potential to be applied in more general queueing models.