A critical aspect, when evaluating the performance of an application, is to make sure that the data we use is accurate. It's quite common for IT systems to experience some transient periods of instabilities; these might occur in many situations such as filling up caches, runtime compilation activities, horizontal scaling, and much more.

A common practice, in performance engineering, is to exclude from the analysis the initial and final part of a performance test to consider only the time when the system is in full operation. Akamas can automatically identify a subset of the whole data to evaluate scores and constraints.

Looking at the example below, from the Online Boutique application, we see that the response time has an initial spike to about 7ms and then stabilizes below 1ms; also the CPU utilization shows a similar pattern.

This is quite common, as an example, for Java-based systems as, in the first minutes of operations activities like heap resizing and just-in-time compilation take place. In this case, Akamas considered in the evaluation of the experiment only the gray area effectively avoiding the impact of the initial spike.

This behavior can be configured in the study by specifying a section called windowing. Two windowing policies allow you to properly configure Akamas in different scenarios.

The simplest policy is called trim and allows users to specify how much time should be excluded from the evaluation from the start and the end of the experiment. It is also possible to apply the trim policy to a specific task of the workflow. This policy can be easily used when, for example, the time required to deploy the application might change. You can read more on this policy in the reference documentation section.

In other contexts, discarding the initial warmup period is not enough. For these scenarios, Akamas supports a more advanced policy, called stability. This policy is also particularly useful for stress tests where our objective is to make the system sustain as much load as possible before becoming unstable as it allows users to express constraints on the stability of the system. You can read more on this policy in the reference documentation section.

The windowing section in the study definition is optional and the default policy considers all the available data to evaluate the performance of the experiment.

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