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Web services, explains how to map the data types and structures into the messages that are exchanged, and includes information that ties the messages to underlying implementations WSDL is defined so that its parts can be developed separately and combined to create a comprehensive WSDL file The data types and structures can be shared among multiple messages, as can the definition of the services exposed within the interface WSDL lists the interfaces and, within an interface, associates each service with an underlying implementation In order to achieve communication for Web services, WSDL maps them onto communication protocols and transports Both parties in a Web services interaction share a common WSDL file The sender uses the WSDL file to generate the message in the appropriate format and to use the appropriate communication protocol The receiver uses the WSDL file to understand how to receive and parse the message and how to map it onto the underlying object or program

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The abstract base class Exception acts as the root of the inheritance tree UserException and SystemException are also abstract base classes; all concrete system exceptions (such as UNKNOWN and BAD_PARAM) are derived from

SystemException, and all user exceptions are derived from UserException The resulting inheritance hierarchy looks like the diagram shown in Figure 78 This exception hierarchy allows you to catch all exceptions in a single catch clause, or you can catch specific exceptions selectively The following code shows an example of handling exceptions for a Thermostat::set_nominal operation:





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measure; this is done by multiplication with a so-called likelihood ratio IS has received substantial theoretical attention; see inter alia [13, 12, 26] and, in the rare event context, [15] or the more up-to-date tutorial [16] IS is one of the most widely used variance reduction technique in general, and for rare event estimation in particular Typical and speci c applications will be more extensively described in Part II of this book The goal of this chapter is to give an overview of the technical framework and the main underlying ideas It is organized as follows Section 22 reviews the very basic notions of IS It describes what the ideal (zero-variance) estimator looks like, and why it is, except in situations where simulation is not needed, infeasible to implement it exactly That section also provides illustrative examples and outlines some properties leading to a good IS estimator, the main message being that the zero-variance estimator has to be approximated as closely as possible In Section 23 the focus is on application of IS in the context of a Markov chain model Since every discrete-event simulation model can be seen as a Markov chain (albeit over a high-dimensional state space), this setting is very general We show how to de ne a zero-variance change of probabilities in that context It is noted that, in general, the zero-variance change of probabilities must depend on the state of the chain We compare this type of change of probabilities with a more restricted class of IS called state-independent, in which the probabilities are changed independently of the current state of the chain This type of state-independent IS originates mainly from asymptotic approximations based on large-deviations theory [3, 15, 25], and has been developed in applications areas such as queuing and nance [10, 15, 16, 19] However, in many situations, any good IS scheme must be state-dependent [3] (as state-independent IS leads to estimators with large, or even in nite, variance) Note that in computational physics (the application area from which it originates) and in reliability, IS has traditionally been state-dependent [5, 14, 16] Finally, Section 24 describes various methods used to approximate the zero-variance (ie, optimal) change of measure Some just use intuitive approximations, whereas others are based on the asymptotic behavior of the system when the events of interest become rarer and rarer (this includes methods based on large-deviations theory, and other techniques as well) Another option is to use adaptive techniques that learn (and use) approximations of the zero-variance change of measure, or optimal parameter values within a class of parameterized IS strategies: the results of completed runs can be used as inputs of strategies for the next runs, but those IS strategies can also be updated at each step of a given run [16, 22] The accuracy assessment of the resulting con dence interval, and the robustness properties of the estimator with respect to rarity, are the focus of the next chapter To avoid overlap, we keep our discussion of those aspects to a minimum here.

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CCS::Thermostat_var ts = ; CCS::TempType new_temp = ; try { ts->set_nominal(new_temp); } catch (const CCS::BadTemp &) { // New temp out of range } catch (const CORBA::UserException &) { // Some other user exception cerr << "User exception" << endl; } catch (const CORBA::OBJECT_NOT_EXIST &) { // Thermostat has been destroyed } catch (const CORBA::SystemException &) { // Some other system exception cerr << "System exception" << endl; } catch () { // Non-CORBA exception -- should never happen }

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