Some results from an asymptotic analysis of a class of simple, circuit-switched networks
Proc. of the international seminar on Teletraffic analysis and computer performance evaluation
Congestion probabilities in a circuit-switched integrated services network
Performance Evaluation
Queueing networks—exact computational algorithms: a unified theory based on decomposition and aggregation
ISDN and Broadband ISDN (2nd ed.)
ISDN and Broadband ISDN (2nd ed.)
Computational algorithms for blocking probabilities in circuit-switched networks
Annals of Operations Research - Special issue on stochastic modeling of telecommunication systems
Blocking with retrials in a completely shared resource environment
Performance Evaluation
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
Exact computation of blocking probabilities in state-dependent multi-facility blocking models
Proceedings of the IFIP WG 7.3 International Conference on Performance of Distributed Systems and Integrated Communication Networks
Sizing a Message Store Subject to Blocking Criteria
Proceedings of the Third International Symposium on Modelling and Performance Evaluation of Computer Systems: Performance of Computer Systems
An inversion algorithm to compute blocking probabilities in loss networks with state-dependent rates
IEEE/ACM Transactions on Networking (TON)
The decomposition of a blocking model for connection-oriented networks
IEEE/ACM Transactions on Networking (TON)
Nonstationary analysis of circuit-switched communication networks
Performance Evaluation
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Three new decomposition methods are developed for the exact analysis of stochastic multi-facility blocking models of the product-form type. The first is a basic decomposition algorithm that reduces the analysis of blocking probabilities to that of two separate subsystems. The second is a generalized M-subsystem decomposition method. The third is a more elaborate and efficient incremental decomposition technique. All of the algorithms exploit the sparsity of locality that can be found in the demand matrix of a system. By reducing the analysis to that of a set of subsystems, the overall dimensionality of the problem is diminished and the computational requirements are reduced significantly. This enables the efficient computation of blocking probabilities in large systems. Several numerical examples are provided to illustrate the computational savings that can be realized.