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Consider the following key points when troubleshooting performance-testing environments: Look for problems in the load-generation clients from which load is simulated. Client machines often produce inaccurate performance-testing results due to insufficient processor or memory resources. Consider adding more client computers to compensate for fast transactions that may cause higher processor utilization; also consider using more memory when this becomes the bottleneck. Memory can be consumed when test data feeds are cached in load generators, or by more complex scripting in load tests.
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Some network interface cards (NICs) when set to auto mode will fail to negotiate with switches in proper full-duplex mode. The result is that the NICs will operate in half-duplex negotiation, which causes inaccurate performance-testing results. A typical perimeter network with a Web server and database server in different layers will be deployed with the Web server having two NICs, one facing your clients and another using a different route to communicate with the database layer. However, be aware that having one NIC in the Web server facing both the clients and the database tier may cause network bottleneck congestion. The database server in the production environment may be using separate hard drives for log files and data files associated with the database as a matter of policy. Replicate such deployment configurations to avoid inaccurate performance-testing results. Consider that if DNS is not properly configured, it might cause broadcast messages to be sent when opening database connections by using the database server name. Name-resolution issues may cause connections to open slowly. Improper data feeds consumed by your scripts will frequently cause you to overlook problems with the environment. For example, low processor activity may be caused by artificial locking due to scripts querying the same record from the database. Consider creating test data feeds that simulate the correct business actions, accounting for variability of data sent from the post request. Load-generation tools may use a central repository such as a database or files in a directory structure to collect performance test data. Make sure that the data repository is located on a machine that will not cause traffic in the route used by your load-generation tools; for example, putting the data repository in the same virtual local-area network (VLAN) of the machine used to manage data collection. Load-generation tools may require the use of special accounts between load-generator machines and the computers that collect performance data. Make sure that you set such configurations correctly. Verify that data collection is occurring in the test environment, taking into consideration that the traffic may be required to pass through a firewall.
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Poor load simulations can render all previous work useless. To understand the data collected from a test run, the load simulation must accurately reflect the test design. When the simulation does not reflect the test design, the results are prone to misinterpretation. Even if your tests accurately reflect the test design, there are still many ways that the test can yield invalid or misleading results. Although it may be tempting to simply trust your tests, it is almost always worth the time and effort to validate the accuracy of your tests before you need to depend on them to provide results intended to assist in making the go-live decision. It may be useful to think about test validation in terms of the following four categories: Test design implementation. To validate that you have implemented your test design accurately (using whatever method you have chosen), you will need to run the test and examine exactly what the test does. Concurrency. After you have validated that your test conforms to the test design when run with a single user, run the test with several users. Ensure that each user is seeded with unique data, and that users begin their activity within a few seconds of
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one another not all at the same second, as this is likely to create an unrealistically stressful situation that would add complexity to validating the accuracy of your test design implementation. One method of validating that tests run as expected with multiple users is to use three test runs; one with 3 users, one with 5 users, and one with 11 users. These three tests have a tendency to expose many common issues with both the configuration of the test environment (such as a limited license being installed on an application component) and the test itself (such as parameterized data not varying as intended). Combinations of tests. Having validated that a test runs as intended with a single user and with multiple users, the next logical step is to validate that the test runs accurately in combination with other tests. Generally, when testing performance, tests get mixed and matched to represent various combinations and distributions of users, activities, and scenarios. If you do not validate that your tests have been both designed and implemented to handle this degree of complexity prior to running critical test projects, you can end up wasting a lot of time debugging your tests or test scripts when you could have been collecting valuable performance information. Test data validation. Once you are satisfied that your tests are running properly, the last critical validation step is to validate your test data. Performance testing can utilize and/or consume large volumes of test data, thereby increasing the likelihood of errors in your dataset. In addition to the data used by your tests, it is important to validate that your tests share that data as intended, and that the application under test is seeded with the correct data to enable your tests.
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