This page describes the Kubernetes HPA v2 and Kubernetes HPA v2 component types. The reference documentation for this HPA can be found here.
With HPA v2 Kubernetes introduced the ability to scale pods on different metrics, to support complex scaling scenarios, such as multiple scaling policies the Kubernetes optimization pack offers two component types:
The Kubernetes HPA v2 component type is used to optimize global HPA parameters, applied to all the scaling policies such as the maximum number of replicas or the cooldown period after a scaling action.
The Kubernetes HPA v2 component type is used to optimize specific scaling policies such as scaling on the CPU utilization or the incoming throughput. To model this scenario you can create multiple components of this type in your system.
| Name | Unit | Description |
|---|---|---|
| Parameter | Type | Unit | Default | Domain | Restart | Description |
|---|---|---|---|---|---|---|
The following tables show a list of constraints that may be required in the definition of the study, depending on the tuned parameters:
| Parameter | Type | Unit | Default | Domain | Restart | Description |
|---|---|---|---|---|---|---|
| Formula | Notes |
|---|---|
k8s_hpa_min_replicas
pods
The lower limit for the number of replicas to which the autoscaler can scale down.
k8s_hpa_max_replicas
pods
The upper limit for the number of replicas to which the autoscaler can scale up.
k8s_desired_replicas
pods
Desired number of replicas of pods managed by this autoscaler, as last calculated by the autoscaler.
k8s_current_replicas
pods
Current number of replicas of pods managed by this autoscaler, as last seen by the autoscaler.
min_replicas
integer
pods
You should select your own default value.
You should select your own domain.
no
The lower limit for the number of replicas to which the autoscaler can scale down. min_replicas is allowed to be 0 if the alpha feature gate HPAScaleToZero is enabled and at least one Object or External metric is configured. Scaling is active as long as at least one metric value is available.
max_replicas
integer
pods
You should select your own default value.
You should select your own domain.
no
The upper limit for the number of replicas to which the autoscaler can scale up. It cannot be less that min_replicas.
scaleDown_stabilizationWindowSeconds
integer
seconds
You should select your own default value.
0 - 3600
no
The number of seconds for which past recommendations should be considered while scaling up or scaling down.
scaleDown_PeriodSeconds
integer
seconds
300
1 - 1800
no
The window of time for which the policy should hold true.
scaleDown_value
integer
pods
You should select your own default value.
You should select your own domain.
no
The amount of change which is permitted by the policy.
scaleUp_stabilizationWindowSeconds
integer
seconds
0
0 - 3600
no
The number of seconds for which past recommendations should be considered while scaling up or scaling down.
scaleUp_PeriodSeconds
integer
seconds
60
1 - 1800
no
The window of time for which the policy should hold true.
scaleUp_value
integer
pods
You should select your own default value.
You should select your own domain.
no
The amount of change which is permitted by the policy.
metric_value
integer
You should select your own default value.
You should select your own domain.
no
The target value of the metric, the semantic depends on the type of the metric (see metric_type).
metric_type
categorical
Utilization
Utilization
Value
AverageValue
no
Represents how to consider the value for scaling. Refer to metrics.*.target.type for more information.
component_name.min_replicas < component_name.max_replicas
Min replicas must be lower than max replicas