Importance sampling for stochastic simulations
Management Science
State space exploration in Markov models
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
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)
Fast Simulation of Excessive Population Size in Tandem Jackson Networks
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Performance Analysis of Communications Networks and Systems
Performance Analysis of Communications Networks and Systems
Analysis of state-independent importance-sampling measures for the two-node tandem queue
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Large deviations and importance sampling for a tandem network with slow-down
Queueing Systems: Theory and Applications
Importance sampling for Jackson networks
Queueing Systems: Theory and Applications
Formalisms for Specifying Markovian Population Models
RP '09 Proceedings of the 3rd International Workshop on Reachability Problems
Rare Event Simulation using Monte Carlo Methods
Rare Event Simulation using Monte Carlo Methods
Asymptotic robustness of estimators in rare-event simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fast Adaptive Uniformization of the Chemical Master Equation
HIBI '09 Proceedings of the 2009 International Workshop on High Performance Computational Systems Biology
Introduction to Rare Event Simulation
Introduction to Rare Event Simulation
| Hi-index | 0.00 |
We address the computation of rare event probabilities in Markovian queueing networks with huge or possibly even infinite state spaces. For this purpose, we incorporate ideas from importance sampling simulations into a non-simulative numerical method that approximates transient probabilities based on a dynamical truncation of the state space. A change of measure technique is applied in order to accomplish a guided state space exploration. Numerical results for three different example networks demonstrate the efficiency and accuracy of our method.