Importance sampling for stochastic simulations
Management Science
Effective bandwidth and fast simulation of ATM intree networks
Performance '93 Proceedings of the 16th IFIP Working Group 7.3 international symposium on Computer performance modeling measurement and evaluation
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)
Estimating small cell-loss ratios in ATM switches via importance sampling
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 32nd conference on Winter simulation
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)
Multilevel Splitting for Estimating Rare Event Probabilities
Operations Research
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
WSC '05 Proceedings of the 37th 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 Markovian networks of series and parallel queues. These heuristics capture state-dependence along the boundaries (when one or more queues are empty) which is critical for the asymptotic optimality of the change of measure. The approach does not require difficult (and often intractable) mathematical analysis or costly optimization involved in adaptive importance sampling methodologies. Experimental results on tandem and parallel 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. Insight drawn from simulating basic networks in this paper promises the applicability of the proposed methodology to larger networks with more general topologies.