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3.1.2
3.1.2
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  • Function
  • Constraints
  • Examples

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  1. Akamas Reference
  2. Construct templates
  3. Study template

Goal & Constraints

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

goal:
  objective: "minimize"
  function:
    formula: "jvm1.response_time + jvm2.response_time"
  constraints:
  - jvm1.memory_used <= 80%
  - jvm1.heap_used <= 3221225472

where:

Field
Type
Value restriction
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.

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:

function:
  formula: "jvm1.response_time / sqrt(x)"
  variables:
    x:
      metric: "throughput"
      labels:
        componentName: "jvm2"
      aggregation: "MAX"

Where:

Field
Type
Value restrictions
Is required
Default value
Description

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.

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

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

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.

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.

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%)

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):

goal:
    objective: "maximize",
    function:
      formula: "jpetstore.elements_per_second"
    constraints:
    - metric: "jpetstore.elements_per_second"
      greaterThan: 55
    - metric: "jpetstore.max_duration"
      lowerThan: 800
    - metric: "jpetstore.avg_duration"
      lowerThan: 70
    - metric: "jpetstore.error_rate"
      lowerThan: 0.01

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).

goal:
  objective: "minimize"
  function:
    formula: "containers_memory_limit/containers_benchmark_duration"
    variables:
      containers_memory_limit:
        metric: "memory_limit"
        labels:
          appId: "app1"
      containers_job_duration:
        metric: "benchmark_duration"
        labels:
          appId: "app1"
        aggregation: "MAX"

Last updated 2 years ago

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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 of the Study.

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

windowing strategy
windowing strategy