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Nicola, V.F. and Zaburnenko, T.S.
(2006)
Efficient Heuristics for the Simulation of Buffer Overflow in Series and Parallel Queueing Networks.
In: Proceedings of the First International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS'2006), 11-13 October 2006, Pisa, Italy.
37.
ACM International Conference Proceeding Series 180.
ACM Press.
ISBN 1-59593-504-5
Full text available as:
Official URL: http://doi.acm.org/10.1145/1190095.1190142  AbstractIn this paper we propose state-dependent importance sampling heuristics to estimate the probability of population overflow in Markovian networks of series and parallel queues. These heuristics capture state-dependence along the boundaries (when one or more queues are empty) which is critical for the asymptotic optimality of the change of measure. The approach does not require dif��?cult (and often intractable) mathematical analysis or costly optimization involved in adaptive importance sampling methodologies. Experimental results on tandem and parallel networks with a moderate number of nodes yield asymptotically ef��?cient estimates (often with bounded relative error) where no other state-independent importance sampling techniques are known to be ef��?cient. Insight drawn from simulating basic networks in this paper promises the applicability of the proposed methodology to larger networks with more general topologies.
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