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Content Provider | IEEE Xplore Digital Library |
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Author | Nicola, V.F. Zaburnenko, T.S. |
Copyright Year | 2006 |
Description | Author affiliation: Fac. of Electr. Eng., Twente Univ., Enschede (Nicola, V.F.; Zaburnenko, T.S.) |
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 difficult mathematical analysis or costly optimization involved in adaptive methodologies. Comprehensive simulations of networks with an arbitrary number of parallel queues and different traffic intensities yield asymptotically efficient estimates (with relative error increasing sub-linearly in the overflow level) where no other state-independent importance sampling techniques are known to be efficient. The efficiency of the proposed heuristic surpasses those based on adaptive importance sampling algorithms, yet it is easier to determine and implement and scales better for large networks |
Starting Page | 398 |
Ending Page | 405 |
File Size | 143649 |
Page Count | 8 |
File Format | |
ISBN | 1424405009 |
DOI | 10.1109/WSC.2006.323108 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-12-03 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Monte Carlo methods Robustness Computational modeling State estimation Optimization methods Yield estimation Synchronous digital hierarchy Feedforward systems Network topology Routing |
Content Type | Text |
Resource Type | Article |
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