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:
    relative:
      - name: memory_used_max
        formula: jvm1.memory_used <= 80%
    absolute:
      - name: heap_used_max
        formula: jvm1.heap_used <= 3221225472

where:

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:

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:

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):

goal:
    objective: "maximize",
    function:
      formula: "jpetstore.elements_per_second"
    constraints:
      absolute:
        - name: min_elements_per_sec
          formula: jpetstore.elements_per_second > 55    
        - name: max_duration
          formula: jpetstore.max_duration < 800    
        - name: min_elements_per_sec
          formula: jpetstore.elements_per_second > 55    
        - name: error_rate
          formula: jpetstore.error_rate < 0.01
      relative:
        - name: avg_duration
          formula: jpetstore.avg_duration < 5%

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"