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In this chapter we have introduced the need for and solution to the workload test phase This stage can be very useful for several goals including: Q Q Q Q Q Q Preventing unexpected runtime situations before going into production Identifying the performance of the application Performance tuning the application and platform Identifying architectural or implementation pitfalls in the application Identifying potential sources of improvement in the application Capacity planning

The v1 layer is a hidden layer For mean-square regression (Section 6-6),9 we are given a training sample of paired of nx-dimensional patterns x and

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We presented the Grinder, a tool to load-test applications We saw how to set up and run HTTP-based load tests with the Grinder, as well as how to analyze the results obtained We also learned how to load-test server-side EJBs and how to extend the Grinder by creating customized plugins Finally, we presented a methodology for stress testing the application The final two chapters will load-test the different versions of the Pizza2Go web site using the Grinder The Wonder Troops will be looking at how the different versions react under stressing and comparing them, to find out which one is the best bet

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In this chapter the Wonder Troops will be conducting y series a load tests using the Grinder to understand how the different versions of their experiments work under stress conditions The first step is to set up the testing environment and prepare the Grindier to create the appropriate stress loads The tests to be performed are the following: Q Q Q Q Q Q Q Selecting ajava Virtual Machine Testing the application developed in 3 Testing the stateless session beans version developed in 4 Testing the stateful session bean version developed in 4 Testing the entity bean version developed in 5 Testing the JMS Point-to-Point application developed in 7 Testing the JMS Publish and Subscribe application developed in 7

Three layers if the input buffer is counted as an extra layer, as many textbooks do Backpropagation networks can also serve as pattern classifiers with a softmax output layer in the manner of Section 6-10 (Example ex6-1lst in the book CD)

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The test environment for these experiments is composed of various machines organized as shown in the diagram below

ny-dimensional target patterns Y We want to generate ny-dimensional output patterns y = y(x) that minimize the sample average of

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