Description
- Abstract:
- This thesis considers a feed-forward network with a single server station serving jobs with multiple levels of priority. The service discipline is preemptive in that the server always serves a job with the current highest priority level. For this system with discontinuous dynamics, we show that the family of scaled state processes satisfy the sample path large deviations principle using a weak convergence argument. In the special case where the jobs have two different levels of priority, we explicitly identify the exponential decay rate of the probability a rare event, namely, the “total population overflow” associated to the feed-forward network. We then use importance sampling -- a variance reduction technique -- efficient for rare event probabilities to simulate the exact probability of interest. The thesis concludes by numerical simulations which confirm our theory.
- Notes:
- Thesis (Ph.D. -- Brown University (2012)
Access Conditions
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- In Copyright
- Restrictions on Use
- Collection is open for research.
Citation
Setayeshgar, Leila,
"Large Deviations for a Feed-forward Network & Importance Sampling for a Single Server Priority Queue"
(2012).
Applied Mathematics Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0QC01TJ
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Applied Mathematics Theses and Dissertations
Theses and Dissertations for the Applied Mathematics department....