After defining the goal and its constraints, the following substep in creating an optimization study is specifying the optimization parameters and metrics. In particular, selecting the parameters that are going to be tuned to optimize the system is a critical decision that requires carefully balancing complexity and effectiveness. As for goals & constraints, also this step may require adopting an iterative approach. See also the Best Practices section here below.
The Parameter selection and Metric selection pages of the Study template section in the reference guide describe how to define the corresponding structure. For offline optimization studies only, the Akamas UI allows the parameters and metrics to be defined as part of the visual procedure activated by the "Create a study" button (see the following figure).
As illustrated by the previous and following figures, during this step is also possible to edit the range of values associated with each optimization parameter with respect to the default domain provided by either the original or custom optimization pack in use for the respective technology.
Please also refer to the Guidelines for choosing optimization parameters for a number of selected technologies. Some examples provided in the Knowledge Base guide may also provide useful guidance.
By default, all parameters specified in the parameters selection of a study are applied ("rendered"). Akamas allows specifying which configuration parameters should be applied in the optimization steps. More precisely:
parameter rendering is available at the step level for baseline, preset, and optimize steps
parameter rendering is not available for bootstrap steps (bootstrapped experiments are not executed)
This feature can be useful to deal with the different strategies through which applications and systems accept configuration parameters.
Please refer to the Parameter rendering page to see how to configure parameter rendering.
The following sections provide some best practices on how to best approach the step of defining optimization parameters. .
Configure parameters domains based on environment specs
Since the parameter domain defines the range of values that the Akamas AI engine can assign to the parameter, when defining the system parameters to be optimized, it is important to review the parameter domains and adjust them based on the system characteristics of the target system, environment and best practices in place.
Akamas optimization packs already provide parameter domains that are correct for most situations. For example, the OpenJDK 11 JVM gcType is a categorical parameter that already includes all the possible garbage collectors that can be set for this JVM version.
For other parameters, there are no sensible default domains as they depend on the environment. For example, the OpenJDK 11 maxHeapSize JVM parameter dictates how much memory the JVM can use. This obviously depends on the environment in which the JVM runs. For example, the upper bound might be 90% of the memory of the virtual machine or container in which the JVM runs.
Defining good parameter domains is important to ensure the parameter configurations suggested by the Akamas AI engine will be as good as possible. Notice that if the domain is not defined correctly, this may cause experiment failures (e.g. the JVM could not start if the maxHeapSize is higher than the container size). As discussed as part of the best practices for defining robust workflows, the Akamas AI engine has been designed to learn configurations that may lead to failures and to automatically discover any hidden constraints found in the environment.
Configure parameter constraints based on Optimization Pack best practices
Depending on the specific technology under optimization, the configuration parameters may have relationships among themselves. For example, in a JVM the newSize parameter defines the size of a region of the JVM heap, and hence its value should be always less than the maxHeapSize parameter.
Akamas AI engine supports the definition of constraints among parameters as this is a frequent need when optimizing real-life applications.
It is important to define the parameter constraints when creating a new study. The optimization pack documentation provides guidelines on what are the most important parameter constraints for the specific technology.
When optimizing a new or custom technology, it may happen that some experiments fail due to unknown parameter constraints being violated. For example, the application may fail to start and only by analyzing the application error logs, the reason for the failure can be understood. For a Java application, the JVM error message (e.g. "new size cannot be larger than max heap size") could provide useful hints. This would reveal that some constraints need to be added to the parameter constraints in the study.
While the Akamas AI engine has been designed to learn from failures, including those due to relationships among parameters that were not explicitly set as constraints, setting parameter constraints may help avoid unnecessary failures and thus speed up the optimization process.
