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How workload modeling improves your performance-test results

Your project is running crazy when—in a last minute call—you’re asked to validate the response time and throughput of the new application before deployment to production.

How will you approach this challenging task? Either you can ask for performance requirements or you can decide to sort it out yourself. For brand new IT services, the former is the best choice. If there’s already a production system, you should consider tool-based workload modeling. But how does this workload modeling actually work?

A methodical approach based on 9 steps:

1. Extract data from prod
2. Identify most frequently used business functions
3. Compare prod and test environments
4. Agree on how to deal with the gaps
5. Calculate the workload
6. Implement the tests
7. Create the test data
8. Execute the tests
9. Receive accurate test results

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Benefits of tool-based workload modeling

Tune for the right volumes. Any uncertainty concerning the number of users and requests to be simulated can lead to inaccuracy; destroying all the good results and insights you captured during your load test. Using a tool-based workload model solves this problem—ensuring you can tune your application for the correct user and data volume.

Scale to fit your environment capacity. Most of the time we’re dealing with gaps in our performance-testing environment. Testing the production user-request volumes on lower-sized testing stages invalidates your test results. Engineers question your defective results due to a lack of confidence due to the existing gaps in the environment. With workload modeling, all parties involved agree on how to deal with the gaps, and they accept the identified breaking points because the earlier analysis brings trust.

Accurate performance baseline. Every change in your environment comes with a potential impact on the reliability of applications. The earlier you identify such changes, the easier they are to fix. A trusted workload model not only improves the accuracy of your test results, it also opens doors for comparisons with previous releases.

To summarize; workload modeling, as one of the most important success factors in your performance-testing pipeline, is often the most overlooked. Don’t forget to give this area the attention it needs!

If you’re unsure about how to make workload modeling or performance engineering part of your value stream, just contact me at any time.

Happy performance testing!

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