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Quantifying the Volume of Application Usage
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If you have access to Web server logs for a current implementation of the application whether it is a production implementation of a previous release, a representative prototype, or a beta release you can use data from these logs to validate and/or enhance the data collected by using the resources above. By performing a quantitative analysis on Web server logs, you can determine: The total number of visits to the site over a period of time (month/week/day). The volume of usage, in terms of total averages and peak loads, on an hourly basis. The duration of sessions for total averages and peak loads on an hourly basis. The total hourly averages and peak loads translated into overlapping user sessions to simulate real scalability volume for the load test. The business cycles or special events that result in significant changes in usage. The following are the inputs and outputs used for determining target load levels.
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Inputs
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Usage data extracted from Web server logs Business volume (both current and projected) mapping to objectives Key scenarios Distribution of work Session characteristics (navigational path, duration, percentage of new users)
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Output
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By combining the volume information with objectives, key scenarios, user delays, navigation paths, and scenario distributions from the previous steps, you can determine the remaining details necessary to implement the workload model under a particular target load.
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Integrating Model Variance
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Because the usage models are best guesses until production data becomes available, it is a good idea to create no fewer than three usage models for each target load. This has the effect of adding a rough confidence interval to the performance measurements. Stakeholders can focus on the results from one test based on many fallible assumptions, as well as on how many inaccuracies in those assumptions are likely to impact the performance characteristics of the application. The three usage models that teams generally find most valuable are: Anticipated Usage (the model or models you created in the Determine Individual User Data and Variance activity) Best Case Usage, in terms of performance (that is, weighted heavily in favor of lowperformance cost activities) Worst Case Usage, in terms of performance (that is, weighted heavily in favor of high-performance cost activities)
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The following chart is an example of the information that testing for all three of these models can provide. As you can see, in this particular case the Anticipated Usage and Best Case Usage resulted in similar performance characteristics. However, the Worst Case Usage showed that there is nearly a 50-percent drop-off in the total load that can be supported between it and the Anticipated Usage. Such information could lead to a reevaluation of the usage model, or possibly to a decision to test with the Worst Case Usage model moving forward as a kind of safety factor until empirical data becomes available.
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Figure 12.5 Usage Models
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Considerations
Consider the following key points when identifying target load levels: Although the volumes resulting from the activities above may or may not end up correlating to the loads the application will actually encounter, the business will want to know if and how well the application as developed or deployed will support its target loads. Because the workload models you have constructed represent the frequency of each activity as a percentage of the total load, you should not need to update your models after determining target load levels. Although it frequently is the case that each workload model will be executed at a variety of load levels and that the load level is very easy to change at run time using most load-generation tools, it is still important to identify the expected and peak target load levels for each workload model for the purpose of predicting or comparing
with production conditions. Changing load levels even slightly can sometimes change results dramatically.
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