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| Content Provider | Springer Nature Link |
|---|---|
| Author | Wang, Hui Dupuis, Paul |
| Copyright Year | 2009 |
| Abstract | Rare event simulation in the context of queueing networks has been an active area of research for more than two decades. A commonly used technique to increase the efficiency of Monte Carlo simulation is importance sampling. However, there are few rigorous results on the design of efficient or asymptotically optimal importance sampling schemes for queueing networks. Using a recently developed game/subsolution approach, we construct simple and efficient state-dependent importance sampling schemes for simulating buffer overflows in stable open Jackson networks. The sampling distributions do not depend on the particular event of interest, and hence overflow probabilities for different events can be estimated simultaneously. A by-product of the analysis is the identification of the minimizing trajectory for the calculus of variation problem that is associated with the sample-path large deviation rate function. |
| Ending Page | 157 |
| Page Count | 45 |
| Starting Page | 113 |
| File Format | |
| ISSN | 02570130 |
| e-ISSN | 15729443 |
| Journal | Queueing Systems |
| Issue Number | 1-2 |
| Volume Number | 62 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2009-06-17 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Jackson networks Systems Theory, Control Rare ever simulation Asymptotic optimality Probability Theory and Stochastic Processes Large deviations Isaacs equations Subsolutions Monte Carlo methods Performance evaluation; queueing; scheduling Operations Research/Decision Theory Production/Logistics Queueing theory Computer Communication Networks Importance sampling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Management Science and Operations Research Computational Theory and Mathematics Computer Science Applications |
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