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).
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 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|
|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: 248481