Efficient Heuristics for Simulating Population Overflow in Parallel Networks


Zaburnenko, Tatiana S. and Nicola, Victor F. (2006) Efficient Heuristics for Simulating Population Overflow in Parallel Networks. In: Russian-Scandinavian Symposium on Probability Theory and Applied Probability, 2006, 26-31 August 2006, Petrozavodsk, Russia (pp. pp. 86-93).

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Abstract:In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in networks of parallel queues. This heuristic approximates the “optimal” state-dependent change of measure without the need for costly optimization involved in other recently proposed adaptive algorithms. Preliminary results from simulations of networks with up to 4 parallel queues and different traffic intensities yield asymptotically efficient estimates (with relative error increasing sublinearly in the overflow level) where state-independent importance sampling is ineffective.
Item Type:Conference or Workshop Item
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Link to this item:http://purl.utwente.nl/publications/63922
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