Goal & Constraints
Last updated
Last updated
Optimization goals and constraints are defined using a YAML manifest with the following structure:
where:
| Field | Type | Value restriction | Is Required | Default value | Description |
|---|---|---|---|---|---|
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:
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 in the formula can be expanded with an aggregation in the form of <variable>:<aggreggation>. A list of available aggregations is available in the section Aggregations.
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:
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.
The constraints field specifies constraints on the metrics of the system under test. For a configuration to be valid with respect to the defined goal, such constraints must be satisfied. Constraints can be defined as absolute or relativeToBaseline.
Each constraint has the form of:
mathematical_operationcomparison_operatorvalue_to_compare
where valid mathematical operations include:
+ - * / ^
min max
sqrt log (log is a natural logarithm)
valid comparison operators include:
> < <= >=
== != (equality, inequality)
and valid values to compare include:
absolute values (e.g, 104343)
percentage values relative to the baseline (e.g, 20%)
As an example, you could define an absolute constraint with the following snippet:
Relative constraints can be defined by adding other constraints under the relativeToBaseline section. In the example below, for the configuration to be considered valid, it's required that the metric jvm.memory_used does not exceed by 80% the value measured in the baseline.
Variables used in the study formula specification and in the constraints definition can include an aggregation. The following aggregations are available: avg, min, max, sum, p90, p95, p99.
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 (containers_benchmark_duration).
| Field | Type | Value restrictions | Is required | Default value | Description |
|---|---|---|---|---|---|
| Field | Type | Value restrictions | Is required | Default value | Description |
|---|---|---|---|---|---|
objective
String
minimize maximize
Yes
How Akamas should evaluate the goodness of a generated configuration: if it should consider good a configuration generated that maximizes function, or a configuration that minimizes it.
function
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.
constraints
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.
formula
String
See formula
Yes
The mathematical expression of what to minimize or maximize to reach the objective of the Study.
variables
Object
See below
No
The specification of additional variables present in the formula.
metric
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.
labels
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 metric. One of these labels is componentName, which contains the name of the Component the metric refers to.
aggregation
String
MAX MIN AVG
No
AVG
The strategy through which data points of the metric should be aggregated within the window produced by the application of the selected windowing strategy. By default, an average is taken.