# Goal & Constraints

Optimization goals and constraints are defined using a YAML manifest with the following structure:

where:

Field | Type | Value restriction | Is Required | Default value | Description |
---|---|---|---|---|---|

| String |
| Yes | How Akamas should evaluate the goodness of a generated configuration: if it should consider good a configuration generated that maximizes | |

| Object | It should have a structure like the one described in Goal function | Yes | The specification of the function to be evaluated to assess the goodness of a configuration generated by Akamas. This function is a function of the metrics of the different Components of the System under test. | |

| List of objects | It should have a structure like the one described in Goal constraints | No | A list of constraints on aggregated metrics of the Components of the System under test for which a generated configuration should not be considered valid. |

### Function

The `function`

field of the Goal of a Study details the characteristics of the function Akamas should minimize or maximize to reach the desired performance objective.

The `function`

field has the following structure:

Where:

Field | Type | Value restrictions | Is required | Default value | Description |
---|---|---|---|---|---|

| String | See formula | Yes | The mathematical expression of what to minimize or maximize to reach the objective of the Study. | |

| Object | See below | No | The specification of additional variables present in the |

#### Formula

The `formula`

field represents the mathematical expression of the performance objective for the Study and contains variables and operators with the following characteristics:

Valid operators are:

`+`

`-`

`*`

`/`

`^`

`sqrt(variable)`

`log(variable)`

`max(variable1, variable2)`

`min(variable1, variable2)`

Valid variables are in the form:

<component_name>.<metric_name>, which correspond directly to metrics of Components of the System under test

<variable_name>, which should match variables specified in the

`variables`

field

Each metric that is directly or indirectly part of the formula of the function of the Goal is aggregated by default by average; more specifically, Akamas computes the average of each metric within the time window specified by the windowing strategy of the Study.

#### Variables

The `variables`

field contains the specification of additional variables present in the formula, variables that can offer more flexibility with respect to directly specifying each metric of each Component in the `formula`

.

Notice: each subfield of variables specifies a variable with its characteristics, the name of the subfield is the name of the variable.

The variable subfield has the following structure:

Field | Type | Value restrictions | Is required | Default value | Description |
---|---|---|---|---|---|

| String | should match the name of a metric defined for the Components of the System under test | Yes | The name of the metric of the Components of the System under test that maps to the variable. | |

| A set of key-value pairs | No | A set of filters based on the values of the labels that are attached to the different data points of the | ||

| String |
| No |
| The strategy through which data points of the |

It is possible to use the notation <component_name>.<metric_name> in the `metric`

field to automatically filter the metric’s data point by that component name is applied.

## Constraints

The `constraints`

field specifies constraints on the metrics of the components of the system under test that need to be satisfied for a configuration to be valid with respect to the defined goal.

Constraints always consider the average value of specified metrics within the time window specified by the windowing strategy of the Study.

Constrainst may be absolute or relative. Absolute constraints set a fixed limit that should not be overcome. Relative constraints set a percentage value (calculated from the baseline value after the baseline experiment runs). Then each constraint (absolute or relative) has a name and a formula. The formula has the same syntax as the goal formula so refer to "formula" section above.

### Examples

The following example refers to a study whose goal is to optimize the throughput of a Java service (jpetstore), that is to maximize the throughput (measured as *elements_per_second*) while keeping errors (*error_rate*) and latency (*avg_duration*, *max_duration*) under control (absolute values):

The following example refers to a study whose goal is to optimize the memory consumption of Docker containers in a microservices application, that is to minimize the average memory consumption of Docker containers within the application of *appId*="app1" by observing memory limits, also normalizing by the maximum duration of a benchmark (container_benchmark_duration).

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