Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Multiservice Loss Models for Broadband Telecommunication Networks
Multiservice Loss Models for Broadband Telecommunication Networks
Estimation of blocking probabilities in cellular networks with dynamic channel assignment
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Efficient Simulation of Blocking Probabilities for Multi-layer Multicast Streams
NETWORKING '02 Proceedings of the Second International IFIP-TC6 Networking Conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; and Mobile and Wireless Communications
An adaptive approach to fast simulation of traffic groomed optical networks
WSC '04 Proceedings of the 36th conference on Winter simulation
An adaptive approach to accelerated evaluation of highly available services
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Modeling bufferless packet-switching networks with packet dependencies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Setwise and filtered gibbs samplers for teletraffic analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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In this paper we consider the problem of estimating blocking probabili ties in the multiservice loss system via simulation, applying the static Monte Carlo method with importance sampling. Earlier approaches to this problem include the use of either a single exponentially twisted version of the steady state distribution of the system or a composite of individual exponentially twisted distributions. Here, a different approach is introduced, where the original estimation problem is first decomposed into independent simpler subproblems, each roughly corresponding to estimating the blocking probability contribution from a single link. Then two importance sampling distributions are presented, which very closely approximate the ideal importance sampling distribution for each subproblem. In both methods, the idea is to try to generate samples directly into the blocking state region. The difference between the methods is that the first method, the inverse convolution method, achieves this exactly, while the second one, using a fitted Gaussian distibution, only approximately. The inverse convolution algorithm, however, has a higher memory requirement. Finally, a dynamic control algorithm is given for optimally allocating the samples between different subproblems. The numerical results demonstrate that the variance reduction obtained with the methods, especially with the inverse convolution method, is tryly remarkable, between 670 and 1,000,000 in the examples under consideration.