Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
SIAM Review
Introduction to Algorithms
On the benefits of using functional transitions and Kronecker algebra
Performance Evaluation
A component-level path-based simulation approach for efficient analysis of large Markov models
WSC '05 Proceedings of the 37th conference on Winter simulation
Perfect simulation of index based routing queueing networks
ACM SIGMETRICS Performance Evaluation Review
Backward coupling in petri nets
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
PEPS2007 - Stochastic Automata Networks Software Tool
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
Perfect Simulation of Stochastic Automata Networks
ASMTA '08 Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications
Reachable State Space Generation for Structured Models which Use Functional Transitions
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
GTAexpress: A Software Package to Handle Kronecker Descriptors
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
Simulation of Markovian models using bootstrap method
Proceedings of the 2010 Summer Computer Simulation Conference
Hi-index | 0.00 |
The analytical solution of large Markovian models is one of the major challenges in performance evaluation. Structured formalisms provide a modular description to tackle state space explosion by presenting memory-efficient solutions based on tensor algebra and specific software tools implement such solutions using iterative methods. However, even these numerical methods become unsuitable when massively large models are considered, i.e., models with more than 100 million states. To deal with such classes of models is possible to find approximations of the stationary solution using simulation of long-run trajectories with perfect sampling methods. The use of these methods prevents usual simulation problems such as initial state setup and burn-in time. Unfortunately, the number of produced samples to establish statistically significant solution remains an open problem. This paper analyzes the sampling process in its extent, proposing a memory-efficient stopping criteria based on a numerical tolerance of the measures of interest. Moreover, we present some memory cost estimations for a classical Markovian model in order to demonstrate the gains of the proposed method.