Efficient Heuristics for the Simulation of Buffer Overflow in Series and Parallel Queueing Networks


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Nicola, Victor F. and Zaburnenko, Tatiana S. (2006) Efficient Heuristics for the Simulation of Buffer Overflow in Series and Parallel Queueing Networks. In: First International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS '2006, 11-13 October 2006, Pisa, Italy (pp. p. 37).

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Abstract:In 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 difficult (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 efficient estimates (often with bounded relative error) where no other state-independent importance sampling techniques are known to be efficient. Insight drawn from simulating basic networks in this paper promises the applicability of the proposed methodology to larger networks with more general topologies.
Item Type:Conference or Workshop Item
Copyright:© 2006 ACM
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Link to this item:http://purl.utwente.nl/publications/63921
Official URL:http://doi.acm.org/10.1145/1190095.1190142
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Metis ID: 237922