Defining optimization steps

A final step in defining an optimization study is to specify specifies the sequence of steps executed while running the study.

The following four types of steps are available:

• Baseline: performs an experiment and sets it as a baseline for all the other ones

• Bootstrap: imports experiments from other studies

• Preset: performs an experiment with a specific configuration

• Optimize: performs experiments and generates optimized configurations

Please notice that at least one baseline step is always required in any optimization study. This applies not only to offline optimization studies, but also to live optimization studies as it is being used to suggest changes to parameter values starting from the default values.

Example of optimization study with two steps: baselining and optimize

Example of optimization study with three steps: baselining, bootstrap and optimize

The Steps page in the Study template section in the reference guide describes how to define the corresponding structures for each of the different types of steps allowed by Akamas. For offline optimization studies only, the Akamas UI allows the optimization steps to be defined as part of the visual procedure activated by the "Create a study" button (see the following figure).

Defining the steps while creating a new study

In addition to the best practices here below, please refer to the section Optimization examples for a number of examples related to a variety of technologies and the Knowledge Base guide for real-world examples.

Best Practices

The following sections provide some best practices on how to best approach the step of defining the baseline step.

Ensure the baseline configuration is correct

In an optimization study, the baseline is an important experiment as it represents the system performance with the current configuration, and serves as a reference to assess the relative improvements the optimization achieved.

Therefore, it is important to make sure the baseline configuration of the study correctly reflects the current configuration - be it the vendor default or the result of a manual tuning exercise.

Evaluate which parameters to include in the baseline configuration

When defining the study baseline configuration it is important to evaluate which parameters to include. Indeed, several technologies have default values assigned to most of their configuration parameters. However, the runtime behavior can be different depending on whether the parameter is set to the default value or it is not set at all.

Therefore, it is recommended to review the current configuration (e.g. the one in place in production) and identify which parameters and values have been set (e.g. JVM maxHeapSize = 2GB, gcType = Parallel, etc.), and then to only set those parameters with their corresponding values, without adding any other parameters. This ensures that the specified baseline is consistent with the real production setup.