Estimating Large Delay Probabilities in Two Correlated Queues
| Autor: | Ewan Jacov Cahen, Michel Mandjes, Bert Zwart |
|---|---|
| Přispěvatelé: | Stochastics (KDV, FNWI), Stochastic Operations Research, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Importance sampling
021103 operations research Logarithm Computer science Stochastic process 0211 other engineering and technologies Logarithmic asymptotics 02 engineering and technology Random walk 01 natural sciences Computer Science Applications 010104 statistics & probability Large deviations Modeling and Simulation Large deviations theory Statistical physics 0101 mathematics Focus (optics) Queue |
| Zdroj: | ACM Transactions on Modeling and Computer Simulation, 28(1):2. Association for Computing Machinery (ACM) ACM Transactions on Modeling and Computer Simulation, 28(1):2. Association for Computing Machinery, Inc ACM Transactions on Modeling and Computer Simulation, 28(1) |
| ISSN: | 1049-3301 |
| Popis: | This article focuses on evaluating the probability that both components of a two-dimensional stochastic process will ever, but not necessarily at the same time, exceed some large level u . An important application is in determining the probability of large delays occurring in two correlated queues. Since exact analysis of this probability seems prohibitive, we focus on deriving asymptotics and on developing efficient simulations techniques. Large deviations theory is used to characterise logarithmic asymptotics. The second part of this article focuses on efficient simulation techniques. Using “nearest-neighbour random walk” as an example, we first show that a “naive” implementation of importance sampling, based on the decay rate, is not asymptotically efficient. A different approach, which we call partitioned importance sampling, is developed and shown to be asymptotically efficient. The results are illustrated through various simulation experiments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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