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# KEP-2021: HPA supports scaling to/from zero pods for object/external metrics

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<!-- toc -->
- [Release Signoff Checklist](#release-signoff-checklist)
- [Summary](#summary)
- [Motivation](#motivation)
  - [Goals](#goals)
  - [Non-Goals](#non-goals)
- [Proposal](#proposal)
  - [User Stories (Optional)](#user-stories-optional)
    - [Story 1: Scale a heavy queue consumer on-demand](#story-1-scale-a-heavy-queue-consumer-on-demand)
  - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
  - [Test Plan](#test-plan)
      - [Prerequisite testing updates](#prerequisite-testing-updates)
      - [Unit tests](#unit-tests)
      - [Integration tests](#integration-tests)
      - [e2e tests](#e2e-tests)
  - [Graduation Criteria](#graduation-criteria)
    - [Alpha](#alpha)
    - [Beta](#beta)
    - [GA](#ga)
  - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
  - [Version Skew Strategy](#version-skew-strategy)
- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire)
  - [Feature Enablement and Rollback](#feature-enablement-and-rollback)
  - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning)
  - [Monitoring Requirements](#monitoring-requirements)
  - [Dependencies](#dependencies)
  - [Scalability](#scalability)
  - [Troubleshooting](#troubleshooting)
- [Implementation History](#implementation-history)
- [Drawbacks](#drawbacks)
- [Alternatives](#alternatives)
- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
<!-- /toc -->

## Release Signoff Checklist

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- [x] (R) KEP approvers have approved the KEP status as `implementable`
- [x] (R) Design details are appropriately documented
- [x] (R) Test plan is in place, giving consideration to SIG Architecture and SIG Testing input (including test refactors)
  - [x] e2e Tests for all Beta API Operations (endpoints)
  - [ ] (R) Ensure GA e2e tests meet requirements for [Conformance Tests](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/conformance-tests.md)
  - [ ] (R) Minimum Two Week Window for GA e2e tests to prove flake free
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- [ ] (R) Production readiness review completed
- [ ] (R) Production readiness review approved
- [x] "Implementation History" section is up-to-date for milestone
- [ ] User-facing documentation has been created in [kubernetes/website], for publication to [kubernetes.io]
- [ ] Supporting documentation—e.g., additional design documents, links to mailing list discussions/SIG meetings, relevant PRs/issues, release notes

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[kubernetes.io]: https://kubernetes.io/
[kubernetes/enhancements]: https://git.k8s.io/enhancements
[kubernetes/website]: https://git.k8s.io/website

## Summary

[Horizontal Pod Autoscaler][] (HPA) automatically scales the number of pods in any resource which supports the `scale` subresource based on observed CPU or memory utilization
(or, with custom metrics support, on some other application-provided metrics) from one to many replicas. This proposal adds support for scaling from zero to many replicas and back to zero for object and external metrics.

Scaling to zero is particularly effective for cost reduction when individual pods demand substantial resource requests, such as dedicated CPUs or GPUs. Since CPU and memory utilization can only be measured on running pods, scaling to zero will be limited to object and external metrics.

[Horizontal Pod Autoscaler]: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/

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## Motivation

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With the addition of scaling based on object and external metrics it became possible to automatically adjust the number of running replicas based on an application
provided metric. A typical use-case case for this is scaling the number of queue consumers based on the length of the consumed queue.

In cases of a frequently idle queue or a less latency-sensitive workload, there is no need to keep one replica running at all times.
Instead, you can dynamically scale to zero replicas, especially for workloads with high resource demands, such as those requiring GPUs.
This approach not only reduces costs but also has significant energy-saving potential, particularly as GPU workloads become more prevalent.
When replicas are scaled to zero, the HPA must also be capable of scaling back up as soon as messages become available.

### Goals

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- Provide scaling to zero replicas for object and external metrics
- Provide scaling from zero replicas for object and external metrics

### Non-Goals

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and make progress.
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- Provide scaling to/from zero replicas for resource metrics
- Provide request buffering at the Kubernetes Service level

## Proposal

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Allow the HPA to scale from and to zero using `minReplicas: 0` and a HPA status condition.

### User Stories (Optional)

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#### Story 1: Scale a heavy queue consumer on-demand

As the operator of a video processing pipeline, I would like to reduce costs. While video processing is CPU intensive, it is not a latency sensitive workload. Therefore I want
my video processing workers to only be created if there is actually a video to be processed and terminated afterwards.

### Notes/Constraints/Caveats (Optional)

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Currently disabling HPA is possible by manually setting the scaled resource to `replicas: 0`. This works as the HPA itself could never reach this state itself.
As `replicas: 0` is now a possible state when using `minReplicas: 0` it can no longer be used to differentiate between manually disabled or automatically scaled to zero.

Additionally the `replicas: 0` state is problematic as updating a HPA object `minReplicas` from `0` to `1` has different behavior. If `replicas` was `0` during the update, HPA
will be disabled for the resource, if it was `> 0`, HPA will continue with the new `minReplicas` value.

To resolve these issues the KEP is introducing an explicit `ScaledToZero` condition inside the `HorizontalPodAutoscalerStatus`. When `ScaledToZero=True` was recorded the HPA will scale
up a workload from `0 ~> 1` and remove the condition `ScaledToZero=True`. If the condition is not found, the HPA maintains the current behavior of performing no change.

When the HPA scales a workload from `1 ~> 0`, it records the `ScaledToZero=True` condition inside the status.

### Risks and Mitigations

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As `ScaledToZero` is no explicit property, applying a new Deployment with `replicas: 0` and HPA `minReplicas: 0` can be confusing as the Deployment will never scale.

This needs should be documented and is detectable by looking at the existing `ScalingActive` condition.

In the future pausing the HPA can become an explicit feature and the implicit pausing via `replicas: 0` can be deprecate to remove this confusing.

## Design Details

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Add `ScaledToZero` as HPA `HorizontalPodAutoscalerConditionType`

```golang
const (
 // ScaledToZero indicates that the HPA controller scaled the workload to zero.
 ScaledToZero HorizontalPodAutoscalerConditionType = "ScaledToZero"
)
```

### Test Plan

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[x] I/we understand the owners of the involved components may require updates to
existing tests to make this code solid enough prior to committing the changes necessary
to implement this enhancement.

Most logic related to this KEP is contained in the HPA controller so the testing of
the various `minReplicas`, `replicas` and `ScaledToZero` should be achievable with unit tests.

Additionally integration tests should be added for enable scale to zero by, setting
 `ScaledToZero: true`, setting `minReplicas: 1` and waiting for `replicas` to become `0` and another test for increasing `minReplicas: 1` and observing that `replicas` became `1` again and confirming that `ScaledToZero: true` has been removed.

##### Prerequisite testing updates

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##### Unit tests

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- `/pkg/controller/podautoscaler`: `2025-02-06` - `96.4`

##### Integration tests

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HPA integration tests are being introduced via <https://github.com/kubernetes/kubernetes/pull/138464>, which includes
a scale-to-zero and back scenario for the `HPAScaleToZero` feature gate. As a follow-up for beta we plan to add
a negative test case asserting that scale-to-zero does not happen when an HPA is configured with a CPU (resource)
metric, since scaling to zero is intentionally limited to object/external metrics.

##### e2e tests

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We expect no non-infra related flakes in the last month as a GA graduation criteria.
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E2E tests under <https://github.com/kubernetes/kubernetes/tree/master/test/e2e/autoscaling> cover scaling down to `0`
and back up from `0` based on an external metric with the `HPAScaleToZero` feature gate enabled.

- `[sig-autoscaling] [Feature:HPA] [Feature:HPAScaleToZero] Horizontal pod autoscaling (scale to zero) should scale down to zero and back up based on external metric value`:
  <https://github.com/kubernetes/kubernetes/blob/master/test/e2e/autoscaling/horizontal_pod_autosclaing_external_metrics.go>

### Graduation Criteria

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#### GA

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**For non-optional features moving to GA, the graduation criteria must include
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[conformance tests]: https://git.k8s.io/community/contributors/devel/sig-architecture/conformance-tests.md

#### Deprecation

- Announce deprecation and support policy of the existing flag
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-->

#### Alpha

- Implement the `ScaleToZero` condition recording
- Ensure that all `minReplicas` state transitions from `0` to `1` are working as expected

#### Beta

- Condition-based scale from/to zero implementation merged
  (<https://github.com/kubernetes/kubernetes/pull/135118>)
- Unit tests cover behavior with the `HPAScaleToZero` feature gate both enabled
  and disabled
- E2E test for scale-to-zero and scale-from-zero based on an external metric
  under `test/e2e/autoscaling` gated on `HPAScaleToZero`
- Integration tests cover scale-to-zero and back
  (<https://github.com/kubernetes/kubernetes/pull/138464>) and a negative case
  ensuring HPAs configured with resource (CPU) metrics are not scaled to zero
- Production readiness review approved for beta
- User-facing documentation updated in `kubernetes/website`

#### GA

- `HPAScaleToZero` feature gate has been enabled by default in beta for at
  least one release without blocking bugs
- Feedback from beta users has been gathered and addressed
- E2E test(s) have been running consistently without flakes

### Upgrade / Downgrade Strategy

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As this KEP changes the allowed values for `minReplicas`, special care is required for the downgrade case to not prevent any kind of updates for HPA objects using `minReplicas: 0`. API validation has accepted `minReplicas: 0` with the `HPAScaleToZero` feature gate enabled since Kubernetes 1.16, so downgrades to any version >= 1.16 will not reject existing HPA objects.

Before downgrading to a version without the condition-based implementation, all HPAs using `minReplicas: 0` should be set to `minReplicas: 1` and their workloads scaled to at least one replica, otherwise workloads currently scaled to `replicas: 0` may remain stuck at `replicas: 0` (the old controller cannot distinguish "manually paused" from "HPA scaled to zero" without the `ScaledToZero` condition).

### Version Skew Strategy

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  when this feature is used?
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  CRI or CNI may require updating that component before the kubelet.
-->

This feature only affects control-plane components (`kube-apiserver` and
`kube-controller-manager`); there is no interaction with the kubelet, CRI, CNI,
or CSI, so node version skew is not relevant.

The relevant skew is between `kube-apiserver` and `kube-controller-manager`:

- **`kube-apiserver` upgraded first, `kube-controller-manager` still on the
  previous version**: users may create or update HPAs with `minReplicas: 0`,
  but the older controller does not understand the `ScaledToZero` condition.
  It will treat `replicas: 0` as "manually paused" and will not scale the
  workload back up from zero. Operators should either avoid `minReplicas: 0`
  until both components are on the new version, or manually scale affected
  workloads back to `replicas >= 1` after the controller is upgraded.

- **`kube-controller-manager` upgraded first, `kube-apiserver` still on the
  previous version**: this is not a supported skew direction in Kubernetes, but
  has no adverse effect in practice, since the older API server will continue
  to reject `minReplicas: 0` and the controller simply never observes HPAs
  that require the new behavior.

The `HPAScaleToZero` feature gate lives in `kube-apiserver` (for validation)
and `kube-controller-manager` (for the condition-based scaling behavior).
Enabling the gate only in one of the two components is effectively a subset of
the skew cases above.

## Production Readiness Review Questionnaire

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### Feature Enablement and Rollback

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-->

###### How can this feature be enabled / disabled in a live cluster?

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Documentation is available on [feature gate lifecycle] and expectations, as
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[feature gate lifecycle]: https://git.k8s.io/community/contributors/devel/sig-architecture/feature-gates.md
[existing list]: https://kubernetes.io/docs/reference/command-line-tools-reference/feature-gates/
-->

- [x] Feature gate (also fill in values in `kep.yaml`)
  - Feature gate name: `HPAScaleToZero`
  - Components depending on the feature gate: `kube-apiserver` (accepting
    `minReplicas: 0` during validation) and `kube-controller-manager` (the
    condition-based scale-from/to-zero behavior)
- [x] Other
  - Describe the mechanism:

    When HPAScaleToZero feature gate is enabled HPA supports scaling to zero pods based on object or external metrics. HPA remains active as long as at least one metric value available.

  - Will enabling / disabling the feature require downtime of the control
    plane?

    No

  - Will enabling / disabling the feature require downtime or reprovisioning
    of a node?

    No

###### Does enabling the feature change any default behavior?

<!--
Any change of default behavior may be surprising to users or break existing
automations, so be extremely careful here.
-->

  HPA creation/update with `minReplicas: 0` is no longer rejected.

###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?

<!--
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Yes. To downgrade the cluster to version that does not support scale-to-zero feature or to disable to feature gate:

1. Make sure there are no hpa objects with minReplicas=0 and maxReplicas=0. Here is a oneliner to update it to 1:

    `$ kubectl get hpa --all-namespaces  --no-headers=true | awk  '{if($6==0) printf "kubectl patch hpa/%s --namespace=%s -p \"{\\\"spec\\\":{\\\"minReplicas\\\":1,\\\"maxReplicas\\\":1}}\"\n", $2, $1 }' | sh`
2. Disable `HPAScaleToZero` feature gate
3. In case step 1. has been omitted, workloads might be stuck with `replicas: 0` and need to be manually scaled up to `replicas: 1` to re-enable autoscaling.

###### What happens if we reenable the feature if it was previously rolled back?

Nothing, the feature can be re-enabled without problems and workload with `replicas: 0` targeted by a HPA will be scaled again.

###### Are there any tests for feature enablement/disablement?

<!--
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-->

Yes. Unit tests in `pkg/controller/podautoscaler/horizontal_test.go` exercise
the HPA controller with the `HPAScaleToZero` feature gate both enabled and
disabled, covering:

- HPA creation with `minReplicas: 0` being rejected by API validation when the
  gate is off and accepted when the gate is on.
- Scaling from zero only occurring when the `ScaledToZero=True` condition is
  present (i.e. recorded by the HPA itself) and the gate is enabled.
- The controller conservatively leaving a workload at `replicas: 0` when the
  gate is off, so manually paused workloads are not disturbed.

An e2e test exists at
[`test/e2e/autoscaling/horizontal_pod_autosclaing_external_metrics.go`](https://github.com/kubernetes/kubernetes/blob/master/test/e2e/autoscaling/horizontal_pod_autosclaing_external_metrics.go)
gated on the `HPAScaleToZero` feature gate via `framework.WithFeatureGate`.
Integration tests covering feature-gate on/off paths are being added in
<https://github.com/kubernetes/kubernetes/pull/138464>.

### Rollout, Upgrade and Rollback Planning

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This section must be completed when targeting beta to a release.
-->

As this is a new field every usage is opt-in. In case the kubernetes version is downgraded, currently scaled to 0 workloads might need to be manually scaled to 1 as the controller would treat them as
paused otherwise.

If a rollback is planned, the following steps should be performed before downgrading the kubernetes version:

1. Make sure there are no hpa objects with minReplicas=0 and maxReplicas=0. Here is a oneliner to update it to 1:

    `$ kubectl get hpa --all-namespaces  --no-headers=true | awk  '{if($6==0) printf "kubectl patch hpa/%s --namespace=%s -p \"{\\\"spec\\\":{\\\"minReplicas\\\":1,\\\"maxReplicas\\\":1}}\"\n", $2, $1 }' | sh`
2. Disable `HPAScaleToZero` feature gate
3. Downgrade the Kubernetes version

###### How can a rollout or rollback fail? Can it impact already running workloads?

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Try to be as paranoid as possible - e.g., what if some components will restart
mid-rollout?

Be sure to consider highly-available clusters, where, for example,
feature flags will be enabled on some API servers and not others during the
rollout. Similarly, consider large clusters and how enablement/disablement
will rollout across nodes.
-->

There are no expected side-effects when the rollout fails as the new `ScaleToZero` condition should only be enabled once the version upgraded completed.

If the `kube-apiserver` has been upgraded before the `kube-controller-manager`, an HPA object has been updated to `minReplicas: 0` and the workload is already scaled down to 0 replicas, you must manually scale the workload to at least one replica.

You can detect this situation in one of two ways:

- Manually, by checking the HPA status and verifying that all entries show ScalingActive set to true and do not mention ScalingDisabled, or

- Automatically, by using the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics)
 to ensure the `ScalingActive` condition is `true.`

If an rollback is attempted, all HPAs should be updated to `minReplicas: 1` as otherwise HPA for deployments with zero replicas will be disabled until
replicas have been raised explicitly to at least `1`.

###### What specific metrics should inform a rollback?

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What signals should users be paying attention to when the feature is young
that might indicate a serious problem?
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If workloads an unexpected number of HPA entities contain a the status `ScalingActive` `false` and mention `ScalingDisable` the feature isn't working as desired and all HPA objects should be updated to > 0 again and their managed workloads should be scaled to at least 1.

This condition can also be detected using the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics), but reason should be manually confirmed for flagged HPA objects.

###### Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested?

<!--
Describe manual testing that was done and the outcomes.
Longer term, we may want to require automated upgrade/rollback tests, but we
are missing a bunch of machinery and tooling and can't do that now.
-->

The condition-based implementation merged for 1.36
(<https://github.com/kubernetes/kubernetes/pull/135118>), so the upgrade and
rollback paths can now be exercised:

- **Upgrade (gate off → on):** existing HPAs are unaffected. `minReplicas: 0`
  only starts being accepted once the gate is enabled on `kube-apiserver`, and
  scale-to-zero only occurs once the gate is enabled on
  `kube-controller-manager`. No `ScaledToZero` condition exists on objects
  created before the upgrade, so the controller takes no new action on them.
- **Downgrade / disablement (gate on → off):** the controller stops scaling
  workloads to zero. Workloads already at `replicas: 0` with a recorded
  `ScaledToZero` condition must be scaled back to `replicas: 1` before the
  downgrade (see the rollback steps above), because the disabled/older
  controller treats `replicas: 0` as manually paused and will not scale it back
  up.
- **Upgrade → downgrade → upgrade:** re-enabling the gate resumes
  scale-from-zero for HPAs still configured with `minReplicas: 0`; no manual
  recovery is required for objects that were left untouched.

This behavior is covered by the integration tests added in
<https://github.com/kubernetes/kubernetes/pull/138464>, which exercise the
feature-gate on/off paths.

###### Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.?

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Even if applying deprecation policies, they may still surprise some users.
-->

No.

### Monitoring Requirements

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This section must be completed when targeting beta to a release.

For GA, this section is required: approvers should be able to confirm the
previous answers based on experience in the field.
-->

###### How can an operator determine if the feature is in use by workloads?

<!--
Ideally, this should be a metric. Operations against the Kubernetes API (e.g.,
checking if there are objects with field X set) may be a last resort. Avoid
logs or events for this purpose.
-->

The new status will be visible inside the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) as
and the `minReplicas: 0` setting reflected in `kube_horizontalpodautoscaler_spec_min_replicas`.

###### How can someone using this feature know that it is working for their instance?

<!--
For instance, if this is a pod-related feature, it should be possible to determine if the feature is functioning properly
for each individual pod.
Pick one more of these and delete the rest.
Please describe all items visible to end users below with sufficient detail so that they can verify correct enablement
and operation of this feature.
Recall that end users cannot usually observe component logs or access metrics.
-->

When this feature is enabled for a workload scaled based on an object or external metric, the workload should be scaled to 0 replicas when the metric is 0.

###### What are the reasonable SLOs (Service Level Objectives) for the enhancement?

<!--
This is your opportunity to define what "normal" quality of service looks like
for a feature.

It's impossible to provide comprehensive guidance, but at the very
high level (needs more precise definitions) those may be things like:
  - per-day percentage of API calls finishing with 5XX errors <= 1%
  - 99% percentile over day of absolute value from (job creation time minus expected
    job creation time) for cron job <= 10%
  - 99.9% of /health requests per day finish with 200 code

These goals will help you determine what you need to measure (SLIs) in the next
question.
-->

No changes to the autoscaling SLOs.

Scaling from `0 ~> 1` is a new path, but it reuses the regular HPA reconcile
loop and is therefore bounded by the existing sync period
(`--horizontal-pod-autoscaler-sync-period`, default `15s`): once the object or
external metric crosses the threshold, the workload is scaled up on the next
reconcile, plus whatever freshness the metrics pipeline adds. The downscale
stabilization window does not apply to scale-up, so no additional delay is
introduced beyond a single sync period.

###### What are the SLIs (Service Level Indicators) an operator can use to determine the health of the service?

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Pick one more of these and delete the rest.
-->

No changes to the autoscaling SLIs.

###### Are there any missing metrics that would be useful to have to improve observability of this feature?

<!--
Describe the metrics themselves and the reasons why they weren't added (e.g., cost,
implementation difficulties, etc.).
-->

No, in regards to this KEP.

### Dependencies

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This section must be completed when targeting beta to a release.
-->

###### Does this feature depend on any specific services running in the cluster?

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Think about both cluster-level services (e.g. metrics-server) as well
as node-level agents (e.g. specific version of CRI). Focus on external or
optional services that are needed. For example, if this feature depends on
a cloud provider API, or upon an external software-defined storage or network
control plane.

For each of these, fill in the following—thinking about running existing user workloads
and creating new ones, as well as about cluster-level services (e.g. DNS):
  - [Dependency name]
    - Usage description:
      - Impact of its outage on the feature:
      - Impact of its degraded performance or high-error rates on the feature:
-->

The addition has the same dependencies as the current autoscaling controller.

### Scalability

<!--
For alpha, this section is encouraged: reviewers should consider these questions
and attempt to answer them.

For beta, this section is required: reviewers must answer these questions.

For GA, this section is required: approvers should be able to confirm the
previous answers based on experience in the field.
-->

###### Will enabling / using this feature result in any new API calls?

<!--
Describe them, providing:
  - API call type (e.g. PATCH pods)
  - estimated throughput
  - originating component(s) (e.g. Kubelet, Feature-X-controller)
Focusing mostly on:
  - components listing and/or watching resources they didn't before
  - API calls that may be triggered by changes of some Kubernetes resources
    (e.g. update of object X triggers new updates of object Y)
  - periodic API calls to reconcile state (e.g. periodic fetching state,
    heartbeats, leader election, etc.)
-->

No, the amount of autoscaling related API calls will remain unchanged. No other components are affected.

###### Will enabling / using this feature result in introducing new API types?

<!--
Describe them, providing:
  - API type
  - Supported number of objects per cluster
  - Supported number of objects per namespace (for namespace-scoped objects)
-->

No, this only modifies the existing API types.

###### Will enabling / using this feature result in any new calls to the cloud provider?

<!--
Describe them, providing:
  - Which API(s):
  - Estimated increase:
-->

No, the amount of autoscaling related cloud provider calls will remain unchanged. No other components are affected.

###### Will enabling / using this feature result in increasing size or count of the existing API objects?

<!--
Describe them, providing:
  - API type(s):
  - Estimated increase in size: (e.g., new annotation of size 32B)
  - Estimated amount of new objects: (e.g., new Object X for every existing Pod)
-->

Yes, one additional boolean field inside the `spec` of every `HorizontalPodAutoscaler` resource.

###### Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs?

<!--
Look at the [existing SLIs/SLOs].

Think about adding additional work or introducing new steps in between
(e.g. need to do X to start a container), etc. Please describe the details.

[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos
-->

No, the are no visible latency changes expected for existing autoscaling operations.

###### Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components?

<!--
Things to keep in mind include: additional in-memory state, additional
non-trivial computations, excessive access to disks (including increased log
volume), significant amount of data sent and/or received over network, etc.
This through this both in small and large cases, again with respect to the
[supported limits].

[supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md
-->

No, the are no visible changes expected for existing autoscaling operations.

###### Can enabling / using this feature result in resource exhaustion of some node resources (PIDs, sockets, inodes, etc.)?

<!--
Focus not just on happy cases, but primarily on more pathological cases
(e.g. probes taking a minute instead of milliseconds, failed pods consuming resources, etc.).
If any of the resources can be exhausted, how this is mitigated with the existing limits
(e.g. pods per node) or new limits added by this KEP?

Are there any tests that were run/should be run to understand performance characteristics better
and validate the declared limits?
-->
No.

### Troubleshooting

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This section must be completed when targeting beta to a release.

For GA, this section is required: approvers should be able to confirm the
previous answers based on experience in the field.

The Troubleshooting section currently serves the `Playbook` role. We may consider
splitting it into a dedicated `Playbook` document (potentially with some monitoring
details). For now, we leave it here.
-->

###### How does this feature react if the API server and/or etcd is unavailable?

Autoscaling will not occur, this is the same as the current behaviour.

###### What are other known failure modes?

<!--
For each of them, fill in the following information by copying the below template:
  - [Failure mode brief description]
    - Detection: How can it be detected via metrics? Stated another way:
      how can an operator troubleshoot without logging into a master or worker node?
    - Mitigations: What can be done to stop the bleeding, especially for already
      running user workloads?
    - Diagnostics: What are the useful log messages and their required logging
      levels that could help debug the issue?
      Not required until feature graduated to beta.
    - Testing: Are there any tests for failure mode? If not, describe why.
-->

- Failed to fetch the relevant object or external metrics.
  - Detection: `ScalingActive: false` condition with `FailedGetExternalMetric` or `FailedGetObjectMetric` reason.
  - Mitigations: manually scale the resource.
  - Diagnostics: Related errors should be printed as the messages of `ScalingActive: false`.
  - Testing: <https://github.com/kubernetes/kubernetes/blob/0e3818e02760afa8ed0bea74c6973f605ca4683c/pkg/controller/podautoscaler/replica_calculator_test.go#L451>

###### What steps should be taken if SLOs are not being met to determine the problem?

Check `metric_computation_duration_seconds` to see which metric encountered the latency issue.
If the latency problem is caused by metrics used for scaling to zero, you can remove those metrics again from your HPA(s).

## Implementation History

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Major milestones in the lifecycle of a KEP should be tracked in this section.
Major milestones might include:
- the `Summary` and `Motivation` sections being merged, signaling SIG acceptance
- the `Proposal` section being merged, signaling agreement on a proposed design
- the date implementation started
- the first Kubernetes release where an initial version of the KEP was available
- the version of Kubernetes where the KEP graduated to general availability
- when the KEP was retired or superseded
-->

- (2019/02/25) Original design doc: <https://github.com/kubernetes/kubernetes/issues/69687#issuecomment-467082733>
- (2019/07/16) Alpha implementation (<https://github.com/kubernetes/kubernetes/pull/74526>) merged for Kubernetes 1.16
- (2026/03/18) Alpha re-implementation (<https://github.com/kubernetes/kubernetes/pull/135118>) merged for Kubernetes 1.36
- (2026/04/21) Targeted for Beta graduation in Kubernetes 1.37

## Drawbacks

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Why should this KEP _not_ be implemented?
-->

## Alternatives

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What other approaches did you consider, and why did you rule them out? These do
not need to be as detailed as the proposal, but should include enough
information to express the idea and why it was not acceptable.
-->

Third-party solutions like [KEDA][] already support scaling to zero for various resource (e.g. [RabbitMQ Queues](https://keda.sh/docs/2.16/scalers/rabbitmq-queue/)). However, these solutions often introduce additional paradigms and complexity. Since Horizontal Pod Autoscaling is already a core feature of Kubernetes and supports scaling to one, adding native support for scaling to zero would be a valuable and low-complexity enhancement.

[KEDA]: https://keda.sh/

## Infrastructure Needed (Optional)

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Use this section if you need things from the project/SIG. Examples include a
new subproject, repos requested, or GitHub details. Listing these here allows a
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