Asymptotic expansions for closed Markovian networks with state-dependent service rates
Journal of the ACM (JACM)
A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Modeling a class of flexible manufacturing systems with reversible routing
Operations Research
Simulation methods for queues: an overview
Queueing Systems: Theory and Applications
Queueing networks—exact computational algorithms: a unified theory based on decomposition and aggregation
Markovian network processes: congestion-dependent routing and processing
Proceedings of the workshop held at the Mathematical Sciences Institute Cornell University on Mathematical theory of queueing systems
Fast algorithms for generating discrete random variates with changing distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Asymptotically optimal importance sampling for product-form queuing networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A BCMP extension to multiserver stations with concurrent classes of customers
SIGMETRICS '86/PERFORMANCE '86 Proceedings of the 1986 ACM SIGMETRICS joint international conference on Computer performance modelling, measurement and evaluation
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
Mean-Value Analysis of Closed Multichain Queuing Networks
Journal of the ACM (JACM)
Queuing Network Models with State-Dependent Routing
Journal of the ACM (JACM)
Solving product form stochastic networks with Monte Carlo summation
WSC' 90 Proceedings of the 22nd conference on Winter simulation
A tree convolution algorithm for the solution of queueing networks
Communications of the ACM
Linearizer: a heuristic algorithm for queueing network models of computing systems
Communications of the ACM
Computational algorithms for closed queueing networks with exponential servers
Communications of the ACM
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Asymptotically optimal importance sampling for product-form queuing networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Efficiency improvement and variance reduction
WSC '94 Proceedings of the 26th conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Rare event simulation in stochastic models
WSC '95 Proceedings of the 27th conference on Winter simulation
Variance reduction applied to product form multiclass queuing networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Towards a polynomial-time randomized algorithm for closed product-form networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Estimation of blocking probabilities in cellular networks with dynamic channel assignment
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Simple bounds for closed queueing networks
Queueing Systems: Theory and Applications
Queueing network analysis: concepts, terminology, and methods
Journal of Systems and Software
Setwise and filtered gibbs samplers for teletraffic analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The performance analysis of an efficient contention resolution for optical packet switching
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Modeling and analysis of multichannel P2P live video systems
IEEE/ACM Transactions on Networking (TON)
Exact analysis of performance models by the Method of Moments
Performance Evaluation
| Hi-index | 0.00 |
Although many closed multiclass queuing networks have a product-form solution, evaluating their performance measures remains nontrivial due to the presence of a normalization constant. We propose the application of Monte Carlo summation in order to determine the normalization constant, throughputs, and gradients of throughputs. A class of importance-sampling functions leads to a decomposition approach, where separate single-class problems are first solved in a setup module, and then the original problem is solved by aggregating the single-class solutions in an execution model. We also consider Monte Carlo methods for evaluating performance measures based on integral representations of the normalization constant; a theory for optimal importance sampling is developed. Computational examples are given that illustrate that the Monte Carlo methods are robust over a wide range of networks and can rapidly solve networks that cannot be handled by the techniques in the existing literature.