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:
    absolute:
      - name: heap_used
        formula: jvm1.heap_used <= 3221225472
    relativeToBaseline:
      - name: memory_used
        formula: jvm1.memory_used <= 80%

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:max)"
  variables:
    x:
      metric: "throughput"
      labels:
        componentName: "jvm2"

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), and 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.

Variables

The variables field contains the specification of additional variables present in the formula, variables that can offer more flexibility compared 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 system under test. For a configuration to be valid for 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:

goal:
  objective: "minimize"
  function:
    formula: "jvm.response_time"
  constraints:
    absolute:
      - name: heap_used
        formula: jvm.heap_used <= 3221225472

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.

goal:
  objective: "minimize"
  function:
    formula: "jvm.response_time"
  constraints:
    absolute:
      - name: heap_used
        formula: jvm.heap_used <= 3221225472
    relativeToBaseline:
      - name: memory_used
        formula: jvm.memory_used <= 80%

Aggregations

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.

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:
      absolute:
        - name: elements_per_second
          formula: "jpetstore.elements_per_second > 55"
        - name: max_duration
          formula: "jpetstore.max_duration < 800"
        - name: avg_duration
          formula: "jpetstore.avg_duration < 70"
        - name: error_rate
          formula: "jpetstore.error_rate < 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 (containers_benchmark_duration).

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