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# KEP-4049: Storage Capacity Scoring of Nodes for Dynamic Provisioning

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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 (Optional)](#story-1-optional)
    - [Story 2 (Optional)](#story-2-optional)
  - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
  - [Modify stateData to be able to store StorageCapacity](#modify-statedata-to-be-able-to-store-storagecapacity)
  - [Get the capacity of nodes for dynamic provisioning](#get-the-capacity-of-nodes-for-dynamic-provisioning)
  - [Scoring of nodes for dynamic provisioning](#scoring-of-nodes-for-dynamic-provisioning)
  - [Conditions for scoring static or dynamic provisioning](#conditions-for-scoring-static-or-dynamic-provisioning)
  - [Feature Gate Consolidation](#feature-gate-consolidation)
  - [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)
  - [Weighting Static Provisioning Scores and Dynamic Provisioning Scores](#weighting-static-provisioning-scores-and-dynamic-provisioning-scores)
- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
<!-- /toc -->

## Release Signoff Checklist

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## Summary

This KEP proposes adding a way to score nodes for dynamic provisioning of PVs. This scoring method is based on storage capacity in the VolumeBinding plugin. 
By considering the amount of free space that nodes have, it is possible to dynamically schedule pods on the node that has the most or least free space.

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## Motivation

Storage capacity needs to be considered when:

- we want to resize after a node-local PV is scheduled. In this case we need to select a node with as much free space as possible. 
- we want to select a node with less free node space to reduce the number of nodes as much as possible.

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### Goals

- To modify the scoring logic to count on dynamic provisioning in addition to the current, considering only static provisioning.

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### Non-Goals

- To change how to score nodes for static provisioning.

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## Proposal

- Node scores based on available space can be taken into account when performing dynamic provisioning.

Cluster admin can configure the scoring logic using a new field in [`VolumeBindingArgs`](https://github.com/kubernetes/kubernetes/blob/1bb62cd27506f86d4b3f71a61a78e892aa2dbca1/pkg/scheduler/apis/config/types_pluginargs.go#L146-L169) of `kubescheduler.config.k8s.io`. The scoring logic is global for the whole cluster and we propose two values:

- Prefer a node with the least allocatable.
- Prefer a node with the maximum allocatable.

Considering the common scenario of local storage, we want to leave room for volume expansion after node allocation. The default setting is to prefer a node with the maximum allocatable.

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#### Story 1 (Optional)

We want to leave room for volume expansion after node allocation. In this case, we want to allocate the node that has the maximum amount of free space. 

#### Story 2 (Optional)

We want to reduce the number of nodes as much as possible to reduce costs when using a cloud environment. In this case, we want to allocate the node that has the smallest amount of sufficiently free space left.

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| Risk                                                                             | Impact | Mitigation                                         |
| -------------------------------------------------------------------------------- | ------ | -------------------------------------------------- |
| Misconfiguration of storage capacity scoring parameters                          | Medium | Provide documentation                              |
| Potential performance overhead due to additional scoring calculations            | Low    | Optimize scoring algorithms                        |
| Loss of optimized scheduling after downgrading to a version without this feature | Medium | Explain the impact of downgrading in documentation |

## Design Details

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We modify the existing VolumeBinding plugin to achieve scoring of nodes for dynamic provisioning.

### Modify stateData to be able to store StorageCapacity

We modify the struct called `PodVolumes` contained in `stateData` to score nodes for dynamic provisioning.

The struct of `stateData` is as follows:

```go
type stateData struct {
	...
	// podVolumesByNode holds the pod's volume information found in the Filter
	// phase for each node
	// it's initialized in the PreFilter phase
	podVolumesByNode map[string]*PodVolumes
	...
}
```

By making the following changes to `PodVolumes`, `CSIStorageCapacity` can be stored.

```diff
+ type DynamicProvision struct {
+ 	PVC      *v1.PersistentVolumeClaim
+ 	Capacity *storagev1.CSIStorageCapacity
+ }

type PodVolumes struct {
	StaticBindings []*BindingInfo
-   DynamicProvisions []*v1.PersistentVolumeClaim
+ 	DynamicProvisions []*DynamicProvision
}
```

### Get the capacity of nodes for dynamic provisioning

Add `CSIStorageCapacity` to the return value of the `volumeBinder.hasEnoughCapacity` method. This returns the `DynamicProvision.Capacity` field in the case of dynamic provisioning.

```diff
- func (b *volumeBinder) hasEnoughCapacity(provisioner string, claim *v1.PersistentVolumeClaim, storageClass *storagev1.StorageClass, node *v1.Node) (bool, error) {
+ func (b *volumeBinder) hasEnoughCapacity(provisioner string, claim *v1.PersistentVolumeClaim, storageClass *storagev1.StorageClass, node *v1.Node) (bool, *storagev1.CSIStorageCapacity, error) {
	quantity, ok := claim.Spec.Resources.Requests[v1.ResourceStorage]
	if !ok {
		// No capacity to check for.
- 		return true, nil
+ 		return true, nil, nil
	}

	// Only enabled for CSI drivers which opt into it.
	driver, err := b.csiDriverLister.Get(provisioner)
	if err != nil {
		if apierrors.IsNotFound(err) {
			// Either the provisioner is not a CSI driver or the driver does not
			// opt into storage capacity scheduling. Either way, skip
			// capacity checking.
- 			return true, nil
+ 			return true, nil, nil
		}
- 		return false, err
+ 		return false, nil, err
	}
	if driver.Spec.StorageCapacity == nil || !*driver.Spec.StorageCapacity {
- 		return true, nil
+ 		return true, nil, nil
	}

	// Look for a matching CSIStorageCapacity object(s).
	// TODO (for beta): benchmark this and potentially introduce some kind of lookup structure (https://github.com/kubernetes/enhancements/issues/1698#issuecomment-654356718).
	capacities, err := b.csiStorageCapacityLister.List(labels.Everything())
	if err != nil {
- 		return false, err
+ 		return false, nil, err
	}

  sizeInBytes := quantity.Value()
	for _, capacity := range capacities {
		if capacity.StorageClassName == storageClass.Name &&
			capacitySufficient(capacity, sizeInBytes) &&
			b.nodeHasAccess(node, capacity) {
			// Enough capacity found.
- 			return true, nil
+ 			return true, capacity, nil
		}
	}

	// TODO (?): this doesn't give any information about which pools where considered and why
	// they had to be rejected. Log that above? But that might be a lot of log output...
	klog.V(4).InfoS("Node has no accessible CSIStorageCapacity with enough capacity for PVC",
		"node", klog.KObj(node), "PVC", klog.KObj(claim), "size", sizeInBytes, "storageClass", klog.KObj(storageClass))
- 	return false, nil
+ 	return false, nil, nil
}
```

### Scoring of nodes for dynamic provisioning

The `Score` method in the current VolumeBinding plug-in scores nodes considering only static provisioning. The scoring applies to every entry in `podVolumes.StaticBindings`.

In this KEP, add the scoring of nodes for dynamic provisioning in the `Score` method of the VolumeBinding plugin. The scoring applies to every entry in `podVolumes.DynamicProvisions` where `Capacity` is not equal to `nil`.

Scoring for dynamic provisioning is executed if there are no `StaticBindings`. In other words, if there is only static provisioning or both static and dynamic provisioning, the scoring will be done as usual for static provisioning. Then, if there is only dynamic provisioning, the following will be set to `classResources` and passed to the `scorer` function:

- `Requested: provision.PVC.Spec.Resources.Requests[v1.ResourceName(v1.ResourceStorage)]`
- `Capacity: CSIStorageCapacity`

By doing this, we can calculate scores to nodes for dynamic provisioning in a way that is based on the `Shape` setting of `VolumeBindingArgs`, and which takes into account the amount of free space the nodes have.

```diff
// Score invoked at the score extension point.
func (pl *VolumeBinding) Score(ctx context.Context, cs *framework.CycleState, pod *v1.Pod, nodeName string) (int64, *framework.Status) {
	if pl.scorer == nil {
		return 0, nil
	}
	state, err := getStateData(cs)
	if err != nil {
		return 0, framework.AsStatus(err)
	}
	podVolumes, ok := state.podVolumesByNode[nodeName]
        if !ok {
		return 0, nil
	}
-       // group by storage class
+
        classResources := make(classResourceMap)
-       for _, staticBinding := range podVolumes.StaticBindings {
-               class := staticBinding.StorageClassName()
-               storageResource := staticBinding.StorageResource()
-               if _, ok := classResources[class]; !ok {
-                       classResources[class] = &StorageResource{
-                               Requested: 0,
-                               Capacity:  0,
+       if len(podVolumes.StaticBindings) != 0 {
+               // group static biding volumes by storage class
+               for _, staticBinding := range podVolumes.StaticBindings {
+                       class := staticBinding.StorageClassName()
+                       storageResource := staticBinding.StorageResource()
+                       if _, ok := classResources[class]; !ok {
+                               classResources[class] = &StorageResource{
+                                       Requested: 0,
+                                       Capacity:  0,
+                               }
+                       }
+                       classResources[class].Requested += storageResource.Requested
+                       classResources[class].Capacity += storageResource.Capacity
+               }
+       } else {
+               // group dynamic biding volumes by storage class
+               for _, provision := range podVolumes.DynamicProvisions {
+                       if provision.Capacity == nil {
+                               continue
+                       }
+                       class := *provision.PVC.Spec.StorageClassName
+                       if _, ok := classResources[class]; !ok {
+                               classResources[class] = &StorageResource{
+                                       Requested: 0,
+                                       Capacity:  0,
+                               }
                        }
+                       requestedQty := provision.PVC.Spec.Resources.Requests[v1.ResourceName(v1.ResourceStorage)]
+                       classResources[class].Requested += requestedQty.Value()
+                       classResources[class].Capacity += provision.Capacity.Capacity.Value()
                }
-               classResources[class].Requested += storageResource.Requested
-               classResources[class].Capacity += storageResource.Capacity
        }
+
        return pl.scorer(classResources), nil
}
```

Users can select the scoring logic from the following options in `VolumeBindingArgs`. The scoring logic is the same among all Pod + PVC(s).

- (a) Prefer a node with the least allocatable.
- (b) Prefer a node with the maximum allocatable.

Considering the common scenario of local storage, we want to leave room for volume expansion after node allocation. The default setting is to prefer a node with the maximum allocatable.

### Conditions for scoring static or dynamic provisioning

About the `Score` function, the score will be calculated with the existing way (only static provisioning is taken into account) if at least one PVC was statically provisioned. Otherwise, the score will be calculated from dynamic provisioning.

Implementation idea:

```diff
func (pl *VolumeBinding) Score(ctx context.Context, cs *framework.CycleState, pod *v1.Pod, nodeName string) (int64, *framework.Status) {
	...

+ 	if len(static) != 0 {
+ 		return static_score, nil;	// Same value as the current method
+ 	} else {
+ 		return dynamic_score, nil;	// Propose in this KEP
+ 	}
- 	return pl.scorer(classResources), nil
}
```

### Feature Gate Consolidation

The `StorageCapacityScoring` feature gate will now control the functionality previously managed by the `VolumeCapacityPriority` feature gate, which will be deprecated. This consolidation focuses on enabling node scoring based on storage capacity, limited to the behaviors necessary for `StorageCapacityScoring`. Specifically, [the utilization shape points](https://github.com/kubernetes/enhancements/tree/49cff2e7c62800d1c87dbf5fac02c506209d1409/keps/sig-storage/1845-prioritization-on-volume-capacity#configuring-the-utilization-shape-points) have been supported because they are required for `StorageCapacityScoring`. However, [the weight of storage class](https://github.com/kubernetes/enhancements/tree/49cff2e7c62800d1c87dbf5fac02c506209d1409/keps/sig-storage/1845-prioritization-on-volume-capacity#configuring-the-weight-of-storage-class) has not been implemented ([ref1](https://github.com/kubernetes/enhancements/blob/10ff969c18772dc82cda02f7e9140c4840413e5d/keps/sig-storage/1845-prioritization-on-volume-capacity/README.md?plain=1#L201-L203), [ref2](https://github.com/kubernetes/kubernetes/pull/96347/files#diff-c4f8e3891e057e1f662eda397ce4f97d29de7d779d8a886ba2ec5a6df6f9d989R44)), and there are no plans to require it for `StorageCapacityScoring`, so it will not be implemented. For more details on the original proposal, see [KEP-1845](https://github.com/kubernetes/enhancements/blob/master/keps/sig-storage/1845-prioritization-on-volume-capacity/README.md).

### 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
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##### Prerequisite testing updates

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The following unit tests have been written. Most are already merged
into the master branch; a minor fix is under review in
[kubernetes/kubernetes#138497](https://github.com/kubernetes/kubernetes/pull/138497).

- [`TestVolumeBinding/storage_capacity_score`](https://github.com/kubernetes/kubernetes/blob/ba49c893f28512d8e134211afa57a854d2ed00c7/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L738):
  Are the scores assigned to nodes for dynamic provisioning appropriate
  for the amount of free space?
- [`TestVolumeBinding/storage_capacity_score_with_static_binds`](https://github.com/kubernetes/kubernetes/blob/ba49c893f28512d8e134211afa57a854d2ed00c7/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L779):
  Are the amount of free space score of nodes for dynamic provisioning
  and the Static Bindings score both functional?
- [`TestVolumeBinding/dynamic_provisioning_with_multiple_PVCs_of_the_same_StorageClass`](https://github.com/kubernetes/kubernetes/blob/ba49c893f28512d8e134211afa57a854d2ed00c7/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L852):
  Are multiple PVCs of the same StorageClass correctly handled in
  scoring, with their total requested storage accounted for?
- [`TestVolumeBinding/prefer_node_with_least_allocatable`](https://github.com/kubernetes/kubernetes/blob/ba49c893f28512d8e134211afa57a854d2ed00c7/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L887):
  When configured to prefer the node with the least available storage,
  is the pod placed on the node with the least available capacity?
- [`TestVolumeBinding/prefer_node_with_maximum_allocatable`](https://github.com/kubernetes/kubernetes/blob/ba49c893f28512d8e134211afa57a854d2ed00c7/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L941):
  Does the default `Shape` setting configure scoring to prefer the node
  with the most available storage?

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The scoring function is tested in test/integration/volumescheduling/storage_capacity_scoring_test.go.

- [test/integration/volumescheduling/storage_capacity_scoring_test.go](https://github.com/kubernetes/kubernetes/blob/89900005eed1fdfe3060680bddb69ea2a17ede22/test/integration/volumescheduling/storage_capacity_scoring_test.go): [integration master](https://testgrid.k8s.io/sig-release-master-blocking#integration-master&include-filter-by-regex=volumescheduling)

##### e2e tests

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- permalinks to the GitHub source code
- links to the periodic job (typically a job owned by the SIG responsible for the feature), filtered by the test name
- a search in the Kubernetes bug triage tool (https://storage.googleapis.com/k8s-triage/index.html)

We expect no non-infra related flakes in the last month as a GA graduation criteria.
If e2e tests are not necessary or useful, explain why.
-->

The following e2e tests are under review in [kubernetes/kubernetes#138497](https://github.com/kubernetes/kubernetes/pull/138497):

- When only static provisioning is available, or a mixture of static provisioning and dynamic provisioning is available:
  - Does it pass traditional tests?
- When only dynamic provisioning is available (single CSI driver case):
  - Is the Pod placed on the node with the largest available space by default?
  - When `VolumeBindingArgs` is set to "Prefer a node with the maximum allocatable", is the Pod placed on the node with the largest available space?
  - Does the Pod placement fail if no node meets the requested size?
  - Even when the Pod is recreated, is the placement in the node performed as expected above?

### Graduation Criteria

<!--
**Note:** *Not required until targeted at a release.*

Define graduation milestones.

These may be defined in terms of API maturity, [feature gate] graduations, or as
something else. The KEP should keep this high-level with a focus on what
signals will be looked at to determine graduation.

Consider the following in developing the graduation criteria for this enhancement:
- [Maturity levels (`alpha`, `beta`, `stable`)][maturity-levels]
- [Feature gate][feature gate] lifecycle
- [Deprecation policy][deprecation-policy]

Clearly define what graduation means by either linking to the [API doc
definition](https://kubernetes.io/docs/concepts/overview/kubernetes-api/#api-versioning)
or by redefining what graduation means.

In general we try to use the same stages (alpha, beta, GA), regardless of how the
functionality is accessed.

[feature gate]: https://git.k8s.io/community/contributors/devel/sig-architecture/feature-gates.md
[maturity-levels]: https://git.k8s.io/community/contributors/devel/sig-architecture/api_changes.md#alpha-beta-and-stable-versions
[deprecation-policy]: https://kubernetes.io/docs/reference/using-api/deprecation-policy/

Below are some examples to consider, in addition to the aforementioned [maturity levels][maturity-levels].

#### Alpha

- Feature implemented behind a feature flag
- Initial e2e tests completed and enabled

#### Beta

- Gather feedback from developers and surveys
- Complete features A, B, C
- Additional tests are in Testgrid and linked in KEP
- More rigorous forms of testing—e.g., downgrade tests and scalability tests
- All functionality completed
- All security enforcement completed
- All monitoring requirements completed
- All testing requirements completed
- All known pre-release issues and gaps resolved

**Note:** Beta criteria must include all functional, security, monitoring, and testing requirements along with resolving all issues and gaps identified

#### GA

- N examples of real-world usage
- N installs
- Allowing time for feedback
- All issues and gaps identified as feedback during beta are resolved

**Note:** GA criteria must not include any functional, security, monitoring, or testing requirements.  Those must be beta requirements.

**Note:** Generally we also wait at least two releases between beta and
GA/stable, because there's no opportunity for user feedback, or even bug reports,
in back-to-back releases.

**For non-optional features moving to GA, the graduation criteria must include
[conformance tests].**

[conformance tests]: https://git.k8s.io/community/contributors/devel/sig-architecture/conformance-tests.md

#### Deprecation

<!--
- Announce deprecation and support policy of the existing flag
- Two versions passed since introducing the functionality that deprecates the flag (to address version skew)
- Address feedback on usage/changed behavior, provided on GitHub issues
- Deprecate the flag
-->

#### Alpha

- Add `StorageCapacityScoring` feature gate
- E2e tests completed

#### Beta

- One release with positive feedback from users

#### GA

- No users complaining about the new behavior

### Upgrade / Downgrade Strategy

<!--
If applicable, how will the component be upgraded and downgraded? Make sure
this is in the test plan.

Consider the following in developing an upgrade/downgrade strategy for this
enhancement:
- What changes (in invocations, configurations, API use, etc.) is an existing
  cluster required to make on upgrade, in order to maintain previous behavior?
- What changes (in invocations, configurations, API use, etc.) is an existing
  cluster required to make on upgrade, in order to make use of the enhancement?
-->

1. Upgrading the cluster to support storage capacity scoring for dynamic provisioning:
   - After the upgrade, the scheduler will be able to score nodes based on their storage capacity for dynamic provisioning. This will involve additional checks and calculations to ensure that nodes with sufficient capacity are prioritized.
   - Existing configurations and API usage will remain compatible, but administrators may need to review and adjust their storage class configurations to fully leverage the new scoring mechanism.

2. Downgrading the cluster to a version without storage capacity scoring for dynamic provisioning:
   - If the cluster is downgraded, the scheduler will revert to the previous behavior where storage capacity scoring for dynamic provisioning is not considered.
   - Any Pods created after the upgrade will still exist, but their scheduling will no longer take storage capacity into account, potentially leading to less optimal placement.
   - No additional changes to invocations or configurations are required, but administrators should be aware that the enhanced scheduling capabilities will be lost.

### Version Skew Strategy

<!--
If applicable, how will the component handle version skew with other
components? What are the guarantees? Make sure this is in the test plan.

Consider the following in developing a version skew strategy for this
enhancement:
- Does this enhancement involve coordinating behavior in the control plane and nodes?
- How does an n-3 kubelet or kube-proxy without this feature available behave when this feature is used?
- How does an n-1 kube-controller-manager or kube-scheduler without this feature available behave when this feature is used?
- Will any other components on the node change? For example, changes to CSI,
  CRI or CNI may require updating that component before the kubelet.
-->

This enhancement is confined to the `kube-scheduler` component.
A `kube-scheduler` at n-1 (without the `StorageCapacityScoring`
feature gate) reverts to the current scoring behavior — scoring for
VolumeBinding based on static provisioning only. This is acceptable
and identical to the previous behavior. No changes are made to
`kubelet`, `kube-proxy`, `kube-controller-manager`, or any
node-level components (CSI, CRI, or CNI).

## Production Readiness Review Questionnaire

<!--

Production readiness reviews are intended to ensure that features merging into
Kubernetes are observable, scalable and supportable; can be safely operated in
production environments, and can be disabled or rolled back in the event they
cause increased failures in production. See more in the PRR KEP at
https://git.k8s.io/enhancements/keps/sig-architecture/1194-prod-readiness.

The production readiness review questionnaire must be completed and approved
for the KEP to move to `implementable` status and be included in the release.

In some cases, the questions below should also have answers in `kep.yaml`. This
is to enable automation to verify the presence of the review, and to reduce review
burden and latency.

The KEP must have a approver from the
[`prod-readiness-approvers`](http://git.k8s.io/enhancements/OWNERS_ALIASES)
team. Please reach out on the
[#prod-readiness](https://kubernetes.slack.com/archives/CPNHUMN74) channel if
you need any help or guidance.
-->

### Feature Enablement and Rollback

<!--
This section must be completed when targeting alpha to a release.
-->

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

<!--
Pick one of these and delete the rest.

Documentation is available on [feature gate lifecycle] and expectations, as
well as the [existing list] of feature gates.

[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: StorageCapacityScoring
  - Components depending on the feature gate: kube-scheduler

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

The scheduling behavior is changed if this function is enabled.

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

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

Yes, this feature can be disabled after it has been enabled by setting the feature gate to false again. In doing so, the scoring for VolumeBinding will revert to the current method. This change won't affect the behavior of existing Pods.

<!--
Describe the consequences on existing workloads (e.g., if this is a runtime
feature, can it break the existing applications?).

Feature gates are typically disabled by setting the flag to `false` and
restarting the component. No other changes should be necessary to disable the
feature.

NOTE: Also set `disable-supported` to `true` or `false` in `kep.yaml`.
-->

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

Re-enabling the feature from a rolled-back state will result in scheduling that considers dynamic provisioning. There will be no impact on existing running Pods.

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

Yes. The unit tests in `TestVolumeBinding` cover both cases:

- Feature gate enabled
  (`fts: feature.Features{EnableStorageCapacityScoring: true}`):
  the scoring tests listed in the [Unit tests](#unit-tests) section
  above (e.g., [`TestVolumeBinding/storage_capacity_score`][scoring-test])
  verify that dynamic provisioning scoring based on
  `CSIStorageCapacity` works correctly.
- Feature gate disabled (no `fts` field, defaulting to `false`):
  test cases such as
  [`TestVolumeBinding/unbound_claims_no_matches`][no-match-test]
  verify that when the feature gate is disabled, `PreScore` returns
  `Skip` for dynamic-provisioning-only scenarios, meaning behavior
  reverts to static provisioning scoring only.

[scoring-test]: https://github.com/kubernetes/kubernetes/blob/c2e3492837be213a2ffc143bb81588d20df91247/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L738
[no-match-test]: https://github.com/kubernetes/kubernetes/blob/c2e3492837be213a2ffc143bb81588d20df91247/pkg/scheduler/framework/plugins/volumebinding/volume_binding_test.go#L198

<!--
The e2e framework does not currently support enabling or disabling feature
gates. However, unit tests in each component dealing with managing data, created
with and without the feature, are necessary. At the very least, think about
conversion tests if API types are being modified.

Additionally, for features that are introducing a new API field, unit tests that
are exercising the `switch` of feature gate itself (what happens if I disable a
feature gate after having objects written with the new field) are also critical.
You can take a look at one potential example of such test in:
https://github.com/kubernetes/kubernetes/pull/97058/files#diff-7826f7adbc1996a05ab52e3f5f02429e94b68ce6bce0dc534d1be636154fded3R246-R282
-->

### Rollout, Upgrade and Rollback Planning

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

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

Turning the feature gate flag on/off only changes scheduling scoring. So there is no possibility of impacting workloads that are already running.

<!--
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.
-->

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

A spike on metric `schedule_attempts_total{result="error|unschedulable"}` when this feature gate is enabled.

<!--
What signals should users be paying attention to when the feature is young
that might indicate a serious problem?
-->

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

This was tested manually before the transition to beta using a cluster with the
TopoLVM CSI driver. The test covered enabling `StorageCapacityScoring` (with the
corresponding CSI driver configuration), disabling it (reverting the CSI driver
configuration), and re-enabling it. In all three steps, Pod scheduling completed
successfully with no unexpected behavior.

<!--
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.
-->

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

Yes, the `VolumeCapacityPriority` feature gate is deprecated in favor of the new `StorageCapacityScoring` feature gate.

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

### Monitoring Requirements

<!--
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?

If enabled, this feature applies to all workloads which uses delay binding PVCs. Also non-zero value of metric `plugin_execution_duration_seconds{plugin="VolumeBinding",extension_point="Score"}` is a sign indicating this feature is in use.
Unfortunately, there is no way to distinguish whether only static provisioning is being considered (the current behavior) or both static and dynamic provisioning are being considered (the new behavior).

<!--
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.
-->

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

By default, pods that use only dynamically provisioned PVCs will be
scheduled to nodes with the most free space. This behavior can be
configured to prefer nodes with the least free space instead.

<!--
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.
-->

- [x] Other (treat as last resort)
  - Details: Check which node the pod was scheduled to (`kubectl get pod <pod> -o wide`)
    and verify it is the node with the most available storage capacity by running
    `kubectl get csistoragecapacities -A`. By default, the VolumeBinding plugin
    prefers the node with the most available capacity for dynamic provisioning.
    This can be configured to prefer the node with the least available capacity
    instead via the `Shape` setting in `VolumeBindingArgs`.

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

Metric `plugin_execution_duration_seconds{plugin="VolumeBinding",extension_point="Score"}` <= 100ms on 90-percentile.

(This follows the same SLO established in KEP-1845, as the additional overhead introduced by this KEP is limited to arithmetic calculations for scoring.)

<!--
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.
-->

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

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

- [X] Metrics
  - Metric name: `plugin_execution_duration_seconds{plugin="VolumeBinding",extension_point="Score"}`
  - [Optional] Aggregation method:
  - Components exposing the metric: kube-scheduler

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

Nothing in particular.

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

### Dependencies

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

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

Yes. This feature depends on a CSI driver that opts into storage capacity
tracking by setting `StorageCapacity: true` in its `CSIDriver` spec. If no
such driver is deployed, the feature has no effect on scheduling.

- CSI driver (with `StorageCapacity: true` in its `CSIDriver` spec)
  - Usage description: The scheduler reads `CSIStorageCapacity` objects
    published by the CSI driver's `external-provisioner` sidecar to
    determine available storage capacity on each node, which is used to
    score nodes for dynamic provisioning.
    - Impact of its outage on the feature: `CSIStorageCapacity` objects are
      no longer updated. Scoring continues based on stale capacity data and
      may place pods on suboptimal nodes.
    - Impact of its degraded performance or high-error rates on the feature:
      Stale or infrequently updated `CSIStorageCapacity` data causes scoring
      to reflect outdated capacity values. Pods may be placed on suboptimal
      nodes, reducing the effectiveness of the feature. Scoring returns to
      accurate behavior once the driver resumes updating the capacity objects.

<!--
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:
-->

### 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?

No.

<!--
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.)
-->

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

No.

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

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

No.

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

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

No.

<!--
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)
-->

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

Yes, it may affect the time taken by scheduling, but the impact is negligible.

The additional overhead introduced by this KEP is limited to arithmetic
calculations in the Score phase: iterating over `DynamicProvisions` per node
and summing the requested and available capacity values. This is equivalent in
complexity to the existing static provisioning scoring. Importantly, the
`CSIStorageCapacity` objects are already fetched and cached during the Filter
phase, so no additional API calls or I/O occur during scoring. The SLO for
`plugin_execution_duration_seconds{plugin="VolumeBinding",extension_point="Score"}`
is therefore the same as established by KEP-1845 (<= 100ms on 90-percentile).

<!--
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
-->

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

No.

<!--
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
-->

###### 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, this feature will not exhaust node resources such as PIDs, sockets, or inodes.

### Troubleshooting

<!--
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?

The behavior in such cases does not change. This proposal only modifies one of the plugins in the kube-scheduler.

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

- [Pods are placed on suboptimal nodes due to stale CSIStorageCapacity data]
  - Detection: Pods end up on nodes that do not match the intended scoring
    strategy configured via the `Shape` setting of `VolumeBindingArgs` — for
    example, placed on a node with less remaining capacity than expected when
    preferring the maximum allocatable. PVC provisioning may also fail after
    scheduling if the actual capacity on the chosen node is insufficient. Run
    `kubectl get csistoragecapacities -A` and inspect the `creationTimestamp`
    to check whether the data is fresh.
  - Mitigations: CSIStorageCapacity objects are created and updated by the
    CSI driver's external-provisioner sidecar, not by the scheduler. If
    the objects do not reflect current node storage state, the root cause
    lies in the external-provisioner. Delete the stale CSIStorageCapacity
    objects so that the external-provisioner recreates them, or restart
    the external-provisioner Pod to trigger a forced resync. As an
    immediate workaround, disable the `StorageCapacityScoring` feature
    gate; scoring for VolumeBinding will revert to the previous method
    and will not be affected by stale capacity information.
  - Diagnostics: Check kube-scheduler logs at verbosity level 5 or higher
    for messages related to CSIStorageCapacity lookups. Verify that
    CSIStorageCapacity objects (`kubectl get csistoragecapacities -A`)
    reflect current node storage state. If objects are stale, also check
    the external-provisioner logs for sync errors or update failures.
  - Testing: Unit and integration tests verify that scoring correctly
    reflects the capacity values reported in CSIStorageCapacity objects.
    Note that detecting staleness is outside the scheduler's responsibility;
    stale capacity data is not covered by tests.

- [Fallback to scoring only with static provisioning when both static
  and dynamic provisioning exist]
  - Detection: When both static and dynamic provisioning are involved,
    the scoring is done only with static provisioning. This is by design
    but may be unexpected for users who expect storage capacity for
    dynamic provisioning to always be scored.
  - Mitigations: This is expected behavior documented in the KEP. Users
    who need scoring for dynamic provisioning should use only dynamically
    provisioned PVCs for a given pod.
  - Diagnostics: Inspect the pod's PVC list. If any PVC has already been
    bound to an existing PV (`kubectl get pvc`), that pod will use scoring
    only with static provisioning.
  - Testing: Unit tests verify that static provisioning takes precedence
    when both exist.

<!--
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.
-->

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

Check the kube-scheduler logs.

## Implementation History

- v1.33: Alpha release
- v1.37: Beta release

<!--
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
-->

## Drawbacks

- The implementation of storage capacity scoring for dynamic provisioning may introduce complexity in the scheduling process. This could potentially lead to increased scheduling latency as the scheduler performs additional checks and calculations.

<!--
Why should this KEP _not_ be implemented?
-->

## Alternatives

<!--
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.
-->

### Weighting Static Provisioning Scores and Dynamic Provisioning Scores

The scoring function will return the sum of the static score and the dynamic score, each multiplied by their respective weights. The weights are determined by the ratio of static and dynamic capacities.

Implementation idea for the `Score` function:

```go
func (pl *VolumeBinding) Score(...) (int64, *framework.Status) {
  ...
  return (static_weight) * static_score + (1-static_weight) * dynamic_score;
}
```

Ultimately, the current design was chosen. The reasons are as follows:

- Conflict issue: In this approach, there is a possibility that the static provisioning and dynamic provisioning scores could cancel each other out, leading to inaccurate scoring.
- Feasibility of implementation: The current design was deemed more feasible and clearer in terms of implementation.

## Infrastructure Needed (Optional)

<!--
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
SIG to get the process for these resources started right away.
-->
