Efficient Importance Sampling Heuristics for the Simulation of Population Overflow in Feed-Forward Queueing Networks

Nicola, Victor F. and Zaburnenko, Tatiana S. (2006) Efficient Importance Sampling Heuristics for the Simulation of Population Overflow in Feed-Forward Queueing Networks. In: Proceedings of the Sixth Rare-Event Simulation Workshop, RESIM 2006, 8-10 October 2006, Bamberg, Germany.

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Abstract:	In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overﬂow in feed-forward networks. This heuristic attempts to approximate the “optimal” state-dependent change of measure without the need for difficult analysis or costly optimization involved in other recently proposed adaptive importance sampling algorithms. Preliminary simulation experiments with a 4-node feed-forward network yield asymptotically efficient estimates, with relative error increasing at most linearly in the overﬂow level, where state-independent importance sampling is demonstrably ineffective.
Item Type:	Conference or Workshop Item
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Link to this item:	http://purl.utwente.nl/publications/63907
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