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 (pp. pp. 144-152).
Restricted to UT campus only : Request a copy
|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|
Electrical Engineering, Mathematics and Computer Science (EEMCS)
|Link to this item:||http://purl.utwente.nl/publications/63907|
|Export this item as:||BibTeX|
Daily downloads in the past month
Monthly downloads in the past 12 months
Repository Staff Only: item control page
Metis ID: 248476