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in the fact that the events under consideration relate to situations whose occurrence can be extremely costly for companies or (network) operators to face Typical examples are: the claims an insurance company is faced with exceed its capital, extremely long delays in call centers that frustrate customers, or a performance collapse in a communication network The common aspect in these examples is that the rare event under consideration can be rephrased as the event that the workload in an appropriately chosen queuing system exceeds a given high threshold, say B Rare events can be analyzed in various ways When the underlying model is Markovian, numerical solution techniques can be used to compute the rare event probability of interest, say the over ow probability (B) The drawback of this approach is that this can be rather time-consuming, as the state space of the Markov chain tends to be large Also, solution techniques of this type just give a numerical output, and do not provide any insight into the impact of the system parameters (such as the arrival rate, service rate, or service requirements) on (B) This motivates the search for asymptotics, that is, (explicit) functions ( ) such that (B)/ (B) 1 as B These asymptotics can be derived in several ways, for instance by applying large-deviations theory [30] A problem, however, is that we often lack error bounds; in other words, we do not know a priori from what B on the approximation (B) (B) is accurate This explains why one often resorts to a simulation-based analysis An important complication is that, using naive simulation, the event under consideration occurs infrequently; consequently, it is time-consuming to obtain reliable estimates Therefore, techniques have had to be developed to speed up the simulation The present chapter is about simulation techniques for estimating small over ow probabilities We remark that excellent textbook treatments of this subject are by Asmussen and Glynn [7, Ch VI] and Bucklew [21, Ch 11] The most frequently used technique is importance sampling (IS); see Hammersley and Handscomb [41] for an early article, and also Hopmans and Kleijnen [44] and Bratley, Fox, and Schrage [19] In IS one simulates the model under an alternative probability measure, and translates the simulation output back to the original measure by multiplying with a so-called likelihood ratio (in this chapter often abbreviated to just likelihood ) Crucial is the choice of the alternative measure, or, more particularly, one would like to nd the measure that provides us with minimum variance The systematic use of large-deviations theory, in the design of ef cient IS algorithms for rare event estimation, started with an in uential paper by Siegmund [70] in the 1970s In many situations [25, 43, 68] this approach led to the identi cation of a new measure that is optimal according to some speci ed criterion Large-deviations arguments also underlie the powerful heuristics of Parekh and Walrand [63], focusing on the estimation of the buffer over ow probability in rather general queuing networks In the 1990s, however, substantial attention was paid to the limitations of this family of results: counterexamples by Glasserman et al [38, 39] indicated that in speci c important cases, the large-deviations based measure performs badly, and can even.

java data matrix reader

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java data matrix reader

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typedef void (__stdcall *PCALLBACK) (ULONG result);

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java data matrix barcode reader

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__declspec(dllexport)

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java data matrix barcode reader

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java data matrix barcode reader

java data matrix reader

java data matrix reader

java data matrix reader

java data matrix barcode reader

java data matrix reader

java data matrix reader

java data matrix barcode reader

java data matrix barcode reader

java data matrix barcode reader

java data matrix reader

java data matrix barcode reader

java data matrix reader