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# Goal & Constraints

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

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goal:

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objective: "minimize"

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function:

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formula: "jvm1.response_time + jvm2.response_time"

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constraints:

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absolute:

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- name: heap_used

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formula: jvm1.heap_used <= 3221225472

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relativeToBaseline:

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- name: memory_used

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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. |

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:1

function:

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formula: "jvm1.response_time / sqrt(x:max)"

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variables:

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x:

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metric: "throughput"

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labels:

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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` . |

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:

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.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_operation*

*comparison_operator*

*value_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%

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):1

goal:

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objective: "maximize"

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function:

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formula: "jpetstore.elements_per_second"

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constraints:

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absolute:

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- name: elements_per_second

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formula: "jpetstore.elements_per_second > 55"

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- name: max_duration

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formula: "jpetstore.max_duration < 800"

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- name: avg_duration

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formula: "jpetstore.avg_duration < 70"

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- name: error_rate

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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).1

goal:

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objective: "minimize"

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function:

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formula: "containers_memory_limit/containers_benchmark_duration:max"

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variables:

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containers_memory_limit:

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metric: "memory_limit"

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labels:

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appId: "app1"

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containers_benchmark_duration:

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metric: "benchmark_duration"

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labels:

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appId: "app1"

Last modified 29d ago