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28. Determine the type of the dispatching target. Two types of completion handlers can be associated with a handle to serve as the target of a proactor's dispatching mechanism, objects and pointers to functions. Implementations of the Proactor pattern can choose the type of dispatching target based on the same criteria described in implementation activity 1.1 of the Reactor (179) pattern. 29. Define the completion handler dispatch interface strategy. We next define the type of interface supported by the completion handler to process completion events. As with the Reactor pattern (179), assuming that we use completion handler objects rather than pointers to functions, two general strategies exist: Single-method dispatch interface strategy. The class diagram in the Structure section illustrates an implementation of the Completion_Handler interface that contains a single event handling method, which we call handle_event(). A proactor uses this method to dispatch completion events to their associated completion handlers. In this case the type of completion event that has occurred is passed as a parameter to the method. The second parameter is the base class for all asynchronous results, which, depending on the completion event, can be further downcast to the correct type.

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the links are analogous to conveyor belts with slots, and slots are allocated to services on demand (see the discussion in Section 2.1.4). We suppose that there is nite buffering at the head of each link, where cells can wait for slots in which to be transmitted. 4.2 Statistical multiplexing Let us consider a service contract with a QoS requirement that the traf c stream should suffer a maximum Cell Loss Probability (CLP). We use the term cell loss , instead of information loss or packet loss , to make implicit a convenient, but not essential, assumption that information is broken into small cells of equal size. A service provider can guarantee CLP D 0 simply by ensuring that on every link the sum of the peak rates of all the connections carried on the link is less than the link s capacity. In other words, for each network link he takes a constraint of the form

TABLE 2.6 n 1 2 3 4 Cost Weighting Cn = cn - c N keep+1 -13477 + 12097 = -1380 -12588 + 12097 = -491 -12363 + 12097 = -266 -12359 + 12097 = -262 Pn 0.575 0.205 0.111 0.109

4. Implement the locking logic. The locking logic serializes access to the critical section of code that is executed just once. To implement this locking logic we can employ the Scoped Locking idiom (345) to ensure that the lock is acquired automatically when the appropriate scope is entered and released automatically when it goes out of scope.

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2002; McCullough 1999a,b), and McCullough (1998) has outlined a benchmarking methodology, but no standard set of benchmark exists. Although no widely accepted set of benchmarks exists, there are a number of libraries and packages that may be used to calculate common statistical distribution with a high degree of accuracy:

In response to an HTTP GET request from a remote Web browser, an HTTP handler might instruct the Windows NT operating system to transmit a large file across a network by calling WriteFile() asynchronously. After the operating system completes the asynchronous operation successfully the resulting completion event indicates the number of bytes transferred to the HTTP handler. The entire file may not be transferred in one WriteFile() operation due to transport-layer flow control. In this case the HTTP handler can invoke another asynchronous WriteFile() operation at the appropriate file offset.

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13 Multiple regression In the previous section we had two possible independent variables which were correlated This meant that either could be used and, indeed, it was only appropriate to use one However, it is often the case that there are two or more independent variables that are not correlated and we wish to understand their overall contribution to the regression This is the domain of multiple regression With two independent variables, instead of tting a line through a set of points as in linear regression, we are now tting a twodimensional plane through a set of points With three independent variables it becomes dif cult to visualize the process, but the mathematical principle of reducing the unexplained variation still applies We will now consider the application of multiple regression to an ecosystem analysis The hypothesis was that methane production from wetlands was being suppressed by acid rain.

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