A preset step performs a single experiment with a specific configuration. The purpose of this step is to help you quickly understand how good is a particular configuration.

A preset step offers two options when selecting the configuration of the experiment to be executed:

• Use a configuration taken from an experiment of a study (can be the same study)

• Use a custom configuration

The preset step has the following structure:

where the from field should have the following structure:

study: "study_preset_1"
experiments: [1]

with

• study contains the name or id of the study from which to take the configuration. in case this is omitted, the same study of the step is considered for experiments from which taking configurations

• experiments contains the number of the experiment from which to take the configuration

Examples

Custom configuration

You can provide a custom configuration by setting values:

name: "my_preset" # name of the step
type: "preset" # type of the step (preset)
values:
  jvm1.maxHeapSize: 1024 # parameter maxHeapSize of jvm1 is set to 1024
  jvm2.maxHeapSize: 2048 # parameter maxHeapSize of jvm2 is set to 2048

Configuration from another study

You can select a configuration taken from another study by setting from:

name: "my_preset"  # name of the step
type: "preset" # type of the step (preset)
from:
  - study: "preset_study_!" # name or id of the study from which to take the configuration
    experiments: [1] # the number of the experiment from which to take the configuration

Configuration from the same study

You can select a configuration taken from the same study by setting from but by omitting the study field:

name: "my_preset"  # name of the step
type: "preset" # type of the step (preset)
from:
  - experiments: [1] # the step will take the configuration of the experiment #1 of the same study of the step

Notice: the from and experiments fields are defined as an array, but can only contain one element.

A baseline step performs an experiment (a baseline experiment) and marks it as the initial experiment of a study. The purpose of the step is to build a reference configuration that Akamas can use to measure the effectiveness of an optimization conducted towards a system.

A baseline step offers three options when it comes to selecting the configuration of the baseline experiment:

• Use a configuration made with the default values of the parameters taken from the system of the study

• Use a configuration taken from an experiment of another study

• Use a custom configuration

The baseline step has the following structure:

where the from field should have the following structure:

study: "study_from_which_to_take_the_baseline"
experiments: [1]

with

• study contains the name or id of the study from which to take the configuration

• experiments contains the number of the experiment from which to take the configuration

Examples

Baseline configuration with default values

Default values for the baseline configuration only require setting the name and type fields:

name: "my_baseline" # name of the step
type: "baseline" # type of the step (baseline)

Baseline configuration from another study

The configuration taken from another study to be used as a baseline only requires setting the from field:

name: "my_baseline" # name of the step
type: "baseline"  # type of the step (baseline)
from:
  - study: "study_from_which_take_the_baseline" # name or id of the study from which to take the configuration
    experiments: [1] # the number of the experiment from which to take the configuration

Notice: the from and experiments fields are defined as an array, but can only contain one element.

Custom baseline configuration

The custom configuration for the baseline only requires setting the values field:

name: "my_baseline" # name of the step
type: "baseline" # type of the step (baseline)
values:
  jvm1.maxHeapSize: 1024 # parameter maxHeapSize of jvm1 is set to 1024
  jvm2.maxHeapSize: 2048 # parameter maxHeapSize of jvm2 is set to 2048

An optimize step generates optimized configurations according to the defined optimization strategy. During this step, Akamas AI is used to generate such optimized configurations.

The optimize step has the following structure:

Optimizer

The optimizer field allows selecting the desired optimizer:

• AKAMAS identifies the standard AI optimizer used by Akamas

• SOBOL identifies an optimizer that generates configurations using Sobol sequences

• RANDOM identifies an optimization that generates configurations using random numbers

Notice that SOBOL and RANDOM optimizers do not perform initialization experiments, hence the field numberOfInitExperiments is ignored.

Refer to page Optimizer Options for more configuration options for the optimizer

Failures

The optimize step is fault-tolerant and tries to relaunch experiments on failure. Nevertheless, the step imposes a limit on the number of failed experiments: if too many experiments fail, then the entire step fails too. By default, at most 30 experiments can fail while Akamas is optimizing systems. An experiment is considered failed when it either failed to run (i.e., there is an error in the workflow) or violated some constraint.

Initializations

The optimize step launches some initialization experiments (by default 10) that do not apply the AI optimizer and are used to find good configurations. By default, the step performs 10 initialization experiments.

Initialization experiments take into account bootstrapped experiments, experiments executed in preset steps, and baseline experiments.

Example

The following snippet shows an optimization step that runs 50 experiments using the SOBOL optimizer:

name: "my_optimize" # name of the step
type: "optimize" # type of the step (optimize)
optimizer: "SOBOL"
numberOfExperiments: 50 # amount of experiments to execute
numberOfTrials: 2 # amount of trials for each experiment

A baseline step performs an experiment (a baseline experiment) and marks it as the initial experiment of a study. The purpose of the step is to build a reference configuration that Akamas can use to measure the effectiveness of an optimization conducted towards a system.

When a bootstrap step imports an experiment from another study, the step copies not only the experiment but also its trials and the system metrics generated during its execution.

The bootstrap step has the following structure:

where the from field should have the following structure:

study: "study_bootstrap_1" 2experiments: [1,2,3]

with:

• study contains the name or id of the study from which to import experiments

• experiments contains the numbers of the experiments from which to import

Examples

The following is an example of a bootstrap step that imports four experiments from two studies:

name: "my_bootstrap" # name of the step
type: "bootstrap" # type of the step (bootstrap)
from:
  - study: "study_bootstap_1" # name or id of the study from which to import
    experiments: [1, 2, 4] # the numbers of the experiments to import
  - study: "study_bootstrap_2"
    experiments: [1]

You can also import all the experiments of a study by omitting the experiments field:

name: "my_bootstrap" # name of the step
type: "bootstrap" # type of the step (bootstrap)
from:
  - study: "study_bootstrap_1" # name or id of the study from which to import

The Steps field in a study specifies the sequence of steps executed while running the study. These steps are run in exactly the same order in which they will be executed.

The following types of steps are available:

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

• : imports experiments from other studies

• : performs an experiment with a specific configuration

• : performs experiments and generates optimized configurations

Notice that the structure corresponding to the steps is different for the different types of steps.