Horizontal Pod Autoscaler
The Horizontal Pod Autoscaler automatically scales the number of pods in a replication controller, deployment, replica set or stateful set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics). Note that Horizontal Pod Autoscaling does not apply to objects that can’t be scaled, for example, DaemonSets.
The Horizontal Pod Autoscaler is implemented as a Kubernetes API resource and a controller. The resource determines the behavior of the controller. The controller periodically adjusts the number of replicas in a replication controller or deployment to match the observed average CPU utilization to the target specified by user.
- How does the Horizontal Pod Autoscaler work?
- API Object
- Support for Horizontal Pod Autoscaler in kubectl
- Autoscaling during rolling update
- Support for cooldown/delay
- Support for multiple metrics
- Support for custom metrics
- Support for metrics APIs
- Support for configurable scaling behavior
- What's next
How does the Horizontal Pod Autoscaler work?
The Horizontal Pod Autoscaler is implemented as a control loop, with a period controlled
by the controller manager’s
--horizontal-pod-autoscaler-sync-period flag (with a default
value of 15 seconds).
During each period, the controller manager queries the resource utilization against the metrics specified in each HorizontalPodAutoscaler definition. The controller manager obtains the metrics from either the resource metrics API (for per-pod resource metrics), or the custom metrics API (for all other metrics).
- For per-pod resource metrics (like CPU), the controller fetches the metrics from the resource metrics API for each pod targeted by the HorizontalPodAutoscaler. Then, if a target utilization value is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each pod. If a target raw value is set, the raw metric values are used directly. The controller then takes the mean of the utilization or the raw value (depending on the type of target specified) across all targeted pods, and produces a ratio used to scale the number of desired replicas.
Please note that if some of the pod’s containers do not have the relevant resource request set, CPU utilization for the pod will not be defined and the autoscaler will not take any action for that metric. See the algorithm details section below for more information about how the autoscaling algorithm works.
For per-pod custom metrics, the controller functions similarly to per-pod resource metrics, except that it works with raw values, not utilization values.
For object metrics and external metrics, a single metric is fetched, which describes the object in question. This metric is compared to the target value, to produce a ratio as above. In the
autoscaling/v2beta2API version, this value can optionally be divided by the number of pods before the comparison is made.
The HorizontalPodAutoscaler normally fetches metrics from a series of aggregated APIs (
metrics.k8s.io API is usually provided by
metrics-server, which needs to be launched separately. See
for instructions. The HorizontalPodAutoscaler can also fetch metrics directly from Heapster.
See Support for metrics APIs for more details.
The autoscaler accesses corresponding scalable controllers (such as replication controllers, deployments, and replica sets) by using the scale sub-resource. Scale is an interface that allows you to dynamically set the number of replicas and examine each of their current states. More details on scale sub-resource can be found here.
From the most basic perspective, the Horizontal Pod Autoscaler controller operates on the ratio between desired metric value and current metric value:
desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]
For example, if the current metric value is
200m, and the desired value
100m, the number of replicas will be doubled, since
200.0 / 100.0 ==
2.0 If the current value is instead
50m, we’ll halve the number of
50.0 / 100.0 == 0.5. We’ll skip scaling if the ratio is
sufficiently close to 1.0 (within a globally-configurable tolerance, from
--horizontal-pod-autoscaler-tolerance flag, which defaults to 0.1).
targetAverageUtilization is specified,
currentMetricValue is computed by taking the average of the given
metric across all Pods in the HorizontalPodAutoscaler’s scale target.
Before checking the tolerance and deciding on the final values, we take
pod readiness and missing metrics into consideration, however.
All Pods with a deletion timestamp set (i.e. Pods in the process of being shut down) and all failed Pods are discarded.
If a particular Pod is missing metrics, it is set aside for later; Pods with missing metrics will be used to adjust the final scaling amount.
When scaling on CPU, if any pod has yet to become ready (i.e. it’s still initializing) or the most recent metric point for the pod was before it became ready, that pod is set aside as well.
Due to technical constraints, the HorizontalPodAutoscaler controller
cannot exactly determine the first time a pod becomes ready when
determining whether to set aside certain CPU metrics. Instead, it
considers a Pod “not yet ready” if it’s unready and transitioned to
unready within a short, configurable window of time since it started.
This value is configured with the
--horizontal-pod-autoscaler-initial-readiness-delay flag, and its default is 30
seconds. Once a pod has become ready, it considers any transition to
ready to be the first if it occurred within a longer, configurable time
since it started. This value is configured with the
--horizontal-pod-autoscaler-cpu-initialization-period flag, and its
default is 5 minutes.
currentMetricValue / desiredMetricValue base scale ratio is then
calculated using the remaining pods not set aside or discarded from above.
If there were any missing metrics, we recompute the average more conservatively, assuming those pods were consuming 100% of the desired value in case of a scale down, and 0% in case of a scale up. This dampens the magnitude of any potential scale.
Furthermore, if any not-yet-ready pods were present, and we would have scaled up without factoring in missing metrics or not-yet-ready pods, we conservatively assume the not-yet-ready pods are consuming 0% of the desired metric, further dampening the magnitude of a scale up.
After factoring in the not-yet-ready pods and missing metrics, we recalculate the usage ratio. If the new ratio reverses the scale direction, or is within the tolerance, we skip scaling. Otherwise, we use the new ratio to scale.
Note that the original value for the average utilization is reported back via the HorizontalPodAutoscaler status, without factoring in the not-yet-ready pods or missing metrics, even when the new usage ratio is used.
If multiple metrics are specified in a HorizontalPodAutoscaler, this
calculation is done for each metric, and then the largest of the desired
replica counts is chosen. If any of these metrics cannot be converted
into a desired replica count (e.g. due to an error fetching the metrics
from the metrics APIs) and a scale down is suggested by the metrics which
can be fetched, scaling is skipped. This means that the HPA is still capable
of scaling up if one or more metrics give a
desiredReplicas greater than
the current value.
Finally, just before HPA scales the target, the scale recommendation is recorded. The
controller considers all recommendations within a configurable window choosing the
highest recommendation from within that window. This value can be configured using the
--horizontal-pod-autoscaler-downscale-stabilization flag, which defaults to 5 minutes.
This means that scaledowns will occur gradually, smoothing out the impact of rapidly
fluctuating metric values.
The Horizontal Pod Autoscaler is an API resource in the Kubernetes
autoscaling API group.
The current stable version, which only includes support for CPU autoscaling,
can be found in the
autoscaling/v1 API version.
The beta version, which includes support for scaling on memory and custom metrics,
can be found in
autoscaling/v2beta2. The new fields introduced in
are preserved as annotations when working with
Support for Horizontal Pod Autoscaler in kubectl
Horizontal Pod Autoscaler, like every API resource, is supported in a standard way by
We can create a new autoscaler using
kubectl create command.
We can list autoscalers by
kubectl get hpa and get detailed description by
kubectl describe hpa.
Finally, we can delete an autoscaler using
kubectl delete hpa.
In addition, there is a special
kubectl autoscale command for easy creation of a Horizontal Pod Autoscaler.
For instance, executing
kubectl autoscale rs foo --min=2 --max=5 --cpu-percent=80
will create an autoscaler for replication set foo, with target CPU utilization set to
and the number of replicas between 2 and 5.
The detailed documentation of
kubectl autoscale can be found here.
Autoscaling during rolling update
Currently in Kubernetes, it is possible to perform a rolling update by using the deployment object, which manages the underlying replica sets for you. Horizontal Pod Autoscaler only supports the latter approach: the Horizontal Pod Autoscaler is bound to the deployment object, it sets the size for the deployment object, and the deployment is responsible for setting sizes of underlying replica sets.
Horizontal Pod Autoscaler does not work with rolling update using direct manipulation of replication controllers, i.e. you cannot bind a Horizontal Pod Autoscaler to a replication controller and do rolling update. The reason this doesn’t work is that when rolling update creates a new replication controller, the Horizontal Pod Autoscaler will not be bound to the new replication controller.
Support for cooldown/delay
When managing the scale of a group of replicas using the Horizontal Pod Autoscaler, it is possible that the number of replicas keeps fluctuating frequently due to the dynamic nature of the metrics evaluated. This is sometimes referred to as thrashing.
Starting from v1.6, a cluster operator can mitigate this problem by tuning
the global HPA settings exposed as flags for the
Starting from v1.12, a new algorithmic update removes the need for the upscale delay.
--horizontal-pod-autoscaler-downscale-stabilization: The value for this option is a duration that specifies how long the autoscaler has to wait before another downscale operation can be performed after the current one has completed. The default value is 5 minutes (
Note: When tuning these parameter values, a cluster operator should be aware of the possible consequences. If the delay (cooldown) value is set too long, there could be complaints that the Horizontal Pod Autoscaler is not responsive to workload changes. However, if the delay value is set too short, the scale of the replicas set may keep thrashing as usual.
Support for multiple metrics
Kubernetes 1.6 adds support for scaling based on multiple metrics. You can use the
version to specify multiple metrics for the Horizontal Pod Autoscaler to scale on. Then, the Horizontal Pod
Autoscaler controller will evaluate each metric, and propose a new scale based on that metric. The largest of the
proposed scales will be used as the new scale.
Support for custom metrics
Note: Kubernetes 1.2 added alpha support for scaling based on application-specific metrics using special annotations. Support for these annotations was removed in Kubernetes 1.6 in favor of the new autoscaling API. While the old method for collecting custom metrics is still available, these metrics will not be available for use by the Horizontal Pod Autoscaler, and the former annotations for specifying which custom metrics to scale on are no longer honored by the Horizontal Pod Autoscaler controller.
Kubernetes 1.6 adds support for making use of custom metrics in the Horizontal Pod Autoscaler.
You can add custom metrics for the Horizontal Pod Autoscaler to use in the
Kubernetes then queries the new custom metrics API to fetch the values of the appropriate custom metrics.
See Support for metrics APIs for the requirements.
Support for metrics APIs
By default, the HorizontalPodAutoscaler controller retrieves metrics from a series of APIs. In order for it to access these APIs, cluster administrators must ensure that:
The API aggregation layer is enabled.
The corresponding APIs are registered:
For resource metrics, this is the
metrics.k8s.ioAPI, generally provided by metrics-server. It can be launched as a cluster addon.
For custom metrics, this is the
custom.metrics.k8s.ioAPI. It’s provided by “adapter” API servers provided by metrics solution vendors. Check with your metrics pipeline, or the list of known solutions. If you would like to write your own, check out the boilerplate to get started.
For external metrics, this is the
external.metrics.k8s.ioAPI. It may be provided by the custom metrics adapters provided above.
trueor unset. Setting this to false switches to Heapster-based autoscaling, which is deprecated.
Support for configurable scaling behavior
v2beta2 API allows scaling behavior to be configured through the HPA
behavior field. Behaviors are specified separately for scaling up and down in
scaleDown section under the
behavior field. A stabilization
window can be specified for both directions which prevents the flapping of the
number of the replicas in the scaling target. Similarly specifing scaling
policies controls the rate of change of replicas while scaling.
One or more scaling policies can be specified in the
behavior section of the spec.
When multiple policies are specified the policy which allows the highest amount of
change is the policy which is selected by default. The following example shows this behavior
while scaling down:
behavior: scaleDown: policies: - type: Pods value: 4 periodSeconds: 60 - type: Percent value: 10 periodSeconds: 60
When the number of pods is more than 40 the second policy will be used for scaling down. For instance if there are 80 replicas and the target has to be scaled down to 10 replicas then during the first step 8 replicas will be reduced. In the next iteration when the number of replicas is 72, 10% of the pods is 7.2 but the number is rounded up to 8. On each loop of the autoscaler controller the number of pods to be change is re-calculated based on the number of current replicas. When the number of replicas falls below 40 the first policy(Pods) is applied and 4 replicas will be reduced at a time.
periodSeconds indicates the length of time in the past for which the policy must hold true.
The first policy allows at most 4 replicas to be scaled down in one minute. The second policy
allows at most 10% of the current replicas to be scaled down in one minute.
The policy selection can be changed by specifying the
selectPolicy field for a scaling
direction. By setting the value to
Min which would select the policy which allows the
smallest change in the replica count. Setting the value to
Disabled completely disabled
scaling in that direction.
The stabilization window is used to retrict the flapping of replicas when the metrics
used for scaling keep fluctuating. The stabilization window is used by the autoscaling
algorithm to consider the computed desired state from the past to prevent scaling. In
the following example the stabilization window is specified for
scaleDown: stabilizationWindowSeconds: 300
When the metrics indicate that the target should be scaled down the algorithm looks into previously computed desired states and uses the highest value from the specified interval. In above example all desired states from the past 5 minutes will be considered.
To use the custom scaling not all fields have to be specified. Only values which need to be customized can be specified. These custom values are merged with default values. The default values match the existing behavior in the HPA algorithm.
behavior: scaleDown: stabilizationWindowSeconds: 300 policies: - type: Percent value: 100 periodSeconds: 15 scaleUp: stabilizationWindowSeconds: 0 policies: - type: Percent value: 100 periodSeconds: 15 - type: Pods value: 4 periodSeconds: 15 selectPolicy: Max
For scaling down the stabilization window is 300 seconds(or the value of the
--horizontal-pod-autoscaler-downscale-stabilization flag if provided). There is only a single policy
for scaling down which allows a 100% of the currently running replicas to be removed which
means the scaling target can be scaled down to the minimum allowed replicas.
For scaling up there is no stabilization window. When the metrics indicate that the target should be
scaled up the target is scaled up immediately. There are 2 policies which. 4 pods or a 100% of the currently
running replicas will be added every 15 seconds till the HPA reaches its steady state.
Example: change downscale stabilization window
To provide a custom downscale stabilization window of 1 minute, the following behavior would be added to the HPA:
behavior: scaleDown: stabilizationWindowSeconds: 60
Example: limit scale down rate
To limit the rate at which pods are removed by the HPA to 10% per minute, the following behavior would be added to the HPA:
behavior: scaleDown: policies: - type: Percent value: 10 periodSeconds: 60
To allow a final drop of 5 pods, another policy can be added and a selection strategy of minimum:
behavior: scaleDown: policies: - type: Percent value: 10 periodSeconds: 60 - type: Pods value: 5 periodSeconds: 60 selectPolicy: Max
Example: disable scale down
selectPolicy value of
Disabled turns off scaling the given direction.
So to prevent downscaling the following policy would be used:
behavior: scaleDown: selectPolicy: Disabled
- Design documentation: Horizontal Pod Autoscaling.
- kubectl autoscale command: kubectl autoscale.
- Usage example of Horizontal Pod Autoscaler.
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