### 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.

Original language | Undefined |
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Title of host publication | The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability |

Pages | 86-93 |

Number of pages | 3 |

Publication status | Published - 2006 |

### Keywords

- EWI-9121
- IR-63922
- METIS-248481

## Cite this

Zaburnenko, T. S., & Nicola, V. F. (2006). Efficient Heuristics for Simulating Population Overflow in Parallel Networks. In

*The 2006 Russian-Scandinavian Symposium on Probability Theory and Applied Probability*(pp. 86-93)