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Workload: 1,000 simultaneous users. Think time: Use a random think time between 1 and 10 seconds in the test script after each operation. Test Duration: Run the test for two days. Expected results: Application hosting process should not recycle because of deadlock or memory consumption. Throughput should not fall below 35 requests per second.
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Response time should not be greater than 7 seconds for 95 percent of total transactions completed. Server busy errors should not be more than 10 percent of the total response because of contention-related issues. Order transactions should not fail during test execution. Database entries should match the Transactions succeeded count.
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Step 6 - Simulate Load
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After you have completed the previous steps to an appropriate degree, you should be ready to simulate the load executing the stress test. Typically, test execution follows these steps: 1. Validate that the test environment matches the configuration that you were expecting and/or designed your test for. 2. Ensure that both the test and the test environment are correctly configured for metrics collection. 3. Before running the test, execute a quick smoke test to make sure that the test script and remote performance counters are working correctly. 4. Reset the system (unless your scenario is to do otherwise) and start a formal test execution. Note: Make sure that the client (a.k.a. load generator) computers that you use to generate load are not overly stressed. Utilization of resources such as processor and memory should remain low enough to ensure that the load-generation environment is not itself a bottleneck.
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Step 7 - Analyze Results
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Analyze the captured data and compare the results against the metric s accepted level. If the results indicate that your required performance levels have not been attained, analyze and fix the cause of the bottleneck. To address observed issues, you might need to do one or more of the following: Perform a design review. Perform a code review. Run stress tests in environments where it is possible to debug possible causes of failures, during test execution. In situations where performance issues are observed, but only under conditions that are deemed to be unlikely enough to warrant tuning at the current time, you may want to consider conducting additional tests to identify an early indicator for the issue in order to avoid unwanted surprises.
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The following are examples of how stress testing is applied in practice: Application stress testing. This type of test typically focuses on more than one transaction on the system under stress, without the isolation of components. With
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application stress testing, you are likely to uncover defects related to data locking and blocking, network congestion, and performance bottlenecks on different components or methods across the entire application. Because the test scope is a single application, it is common to use this type of stress testing after a robust application load-testing effort, or as a last test phase for capacity planning. It is also common to find defects related to race conditions and general memory leaks from shared code or components. Transactional stress testing. Transactional stress tests aim at working at a transactional level with load volumes that go beyond those of the anticipated production operations. These tests are focused on validating behavior under stressful conditions, such as high load with same resource constraints, when testing the entire application. Because the test isolates an individual transaction, or group of transactions, it allows for a very specific understanding of throughput capacities and other characteristics for individual components without the added complication of inter-component interactions that occurs in testing at the application level. These tests are useful for tuning, optimizing, and finding error conditions at the specific component level. Systemic stress testing. In this type of test, stress or extreme load conditions are generated across multiple applications running on the same system, thereby pushing the boundaries of the applications expected capabilities to an extreme. The goal of systemic stress testing is to uncover defects in situations where different applications block one another and compete for system resources such as memory, processor cycles, disk space, and network bandwidth. This type of testing is also known as integration stress testing or consolidation stress testing. In large-scale systemic stress tests, you stress all of the applications together in the same consolidated environment. Some organizations choose to perform this type of testing in a larger test lab facility, sometimes with the hardware or software vendor s assistance.
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