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)
Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Efficient simulation of a tandem Jackson network
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Combining importance sampling and temporal difference control variates to simulate Markov Chains
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Efficient simulation of buffer overflow probabilities in jackson networks with feedback
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Importance Sampling Simulation of Population Overflow in Two-node Tandem Networks
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Efficient heuristics for the simulation of population overflow in series and parallel queues
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Efficient simulation of population overflow in parallel queues
Proceedings of the 38th conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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In this paper we propose state-dependent importance sampling heuristics to estimate the probability of population overflow in Jackson networks with arbitrary routing. These heuristics approximate the "optimal" state-dependent change of measure without the need for costly optimization involved in other recently proposed adaptive algorithms. Experimental results on tandem, feed-forward and feed-back networks with a moderate number of nodes yield asymptotically efficient estimates (often with bounded relative error) where no other state-independent importance sampling techniques are known to be efficient.