From fd731edd36ebb568ea749bb64d97c9b3359bf266 Mon Sep 17 00:00:00 2001
From: Brandon Salmon <bsalmon@google.com>
Date: Wed, 1 Oct 2025 23:09:05 +0000
Subject: [PATCH 1/5] KEP-5598: Opportunistic scheduling cache

---
 .../5598-scheduling-cache/README.md           | 886 ++++++++++++++++++
 .../5598-scheduling-cache/kep.yaml            |  43 +
 2 files changed, 929 insertions(+)
 create mode 100644 keps/sig-scheduling/5598-scheduling-cache/README.md
 create mode 100644 keps/sig-scheduling/5598-scheduling-cache/kep.yaml

diff --git a/keps/sig-scheduling/5598-scheduling-cache/README.md b/keps/sig-scheduling/5598-scheduling-cache/README.md
new file mode 100644
index 00000000000..817a48a75fd
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+++ b/keps/sig-scheduling/5598-scheduling-cache/README.md
@@ -0,0 +1,886 @@
+<!--
+**Note:** When your KEP is complete, all of these comment blocks should be removed.
+
+Follow the guidelines of the [documentation style guide].
+In particular, wrap lines to a reasonable length, to make it
+easier for reviewers to cite specific portions, and to minimize diff churn on
+updates.
+
+[documentation style guide]: https://github.com/kubernetes/community/blob/master/contributors/guide/style-guide.md
+
+To get started with this template:
+
+- [ ] **Pick a hosting SIG.**
+  Make sure that the problem space is something the SIG is interested in taking
+  up. KEPs should not be checked in without a sponsoring SIG.
+- [ ] **Create an issue in kubernetes/enhancements**
+  When filing an enhancement tracking issue, please make sure to complete all
+  fields in that template. One of the fields asks for a link to the KEP. You
+  can leave that blank until this KEP is filed, and then go back to the
+  enhancement and add the link.
+- [ ] **Make a copy of this template directory.**
+  Copy this template into the owning SIG's directory and name it
+  `NNNN-short-descriptive-title`, where `NNNN` is the issue number (with no
+  leading-zero padding) assigned to your enhancement above.
+- [ ] **Fill out as much of the kep.yaml file as you can.**
+  At minimum, you should fill in the "Title", "Authors", "Owning-sig",
+  "Status", and date-related fields.
+- [ ] **Fill out this file as best you can.**
+  At minimum, you should fill in the "Summary" and "Motivation" sections.
+  These should be easy if you've preflighted the idea of the KEP with the
+  appropriate SIG(s).
+- [ ] **Create a PR for this KEP.**
+  Assign it to people in the SIG who are sponsoring this process.
+- [ ] **Merge early and iterate.**
+  Avoid getting hung up on specific details and instead aim to get the goals of
+  the KEP clarified and merged quickly. The best way to do this is to just
+  start with the high-level sections and fill out details incrementally in
+  subsequent PRs.
+
+Just because a KEP is merged does not mean it is complete or approved. Any KEP
+marked as `provisional` is a working document and subject to change. You can
+denote sections that are under active debate as follows:
+
+```
+<<[UNRESOLVED optional short context or usernames ]>>
+Stuff that is being argued.
+<<[/UNRESOLVED]>>
+```
+
+When editing KEPS, aim for tightly-scoped, single-topic PRs to keep discussions
+focused. If you disagree with what is already in a document, open a new PR
+with suggested changes.
+
+One KEP corresponds to one "feature" or "enhancement" for its whole lifecycle.
+You do not need a new KEP to move from beta to GA, for example. If
+new details emerge that belong in the KEP, edit the KEP. Once a feature has become
+"implemented", major changes should get new KEPs.
+
+The canonical place for the latest set of instructions (and the likely source
+of this file) is [here](/keps/NNNN-kep-template/README.md).
+
+**Note:** Any PRs to move a KEP to `implementable`, or significant changes once
+it is marked `implementable`, must be approved by each of the KEP approvers.
+If none of those approvers are still appropriate, then changes to that list
+should be approved by the remaining approvers and/or the owning SIG (or
+SIG Architecture for cross-cutting KEPs).
+-->
+# KEP-5598: Opportunistic scheduling cache
+
+<!--
+This is the title of your KEP. Keep it short, simple, and descriptive. A good
+title can help communicate what the KEP is and should be considered as part of
+any review.
+-->
+
+<!--
+A table of contents is helpful for quickly jumping to sections of a KEP and for
+highlighting any additional information provided beyond the standard KEP
+template.
+
+Ensure the TOC is wrapped with
+  <code>&lt;!-- toc --&rt;&lt;!-- /toc --&rt;</code>
+tags, and then generate with `hack/update-toc.sh`.
+-->
+
+<!-- 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](#story-1)
+    - [Story 2](#story-2)
+  - [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)
+  - [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
+
+<!--
+**ACTION REQUIRED:** In order to merge code into a release, there must be an
+issue in [kubernetes/enhancements] referencing this KEP and targeting a release
+milestone **before the [Enhancement Freeze](https://git.k8s.io/sig-release/releases)
+of the targeted release**.
+
+For enhancements that make changes to code or processes/procedures in core
+Kubernetes—i.e., [kubernetes/kubernetes], we require the following Release
+Signoff checklist to be completed.
+
+Check these off as they are completed for the Release Team to track. These
+checklist items _must_ be updated for the enhancement to be released.
+-->
+
+Items marked with (R) are required *prior to targeting to a milestone / release*.
+
+- [ ] (R) Enhancement issue in release milestone, which links to KEP dir in [kubernetes/enhancements] (not the initial KEP PR)
+- [ ] (R) KEP approvers have approved the KEP status as `implementable`
+- [ ] (R) Design details are appropriately documented
+- [ ] (R) Test plan is in place, giving consideration to SIG Architecture and SIG Testing input (including test refactors)
+  - [ ] 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
+- [ ] (R) Graduation criteria is in place
+  - [ ] (R) [all GA Endpoints](https://github.com/kubernetes/community/pull/1806) must be hit by [Conformance Tests](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/conformance-tests.md) within one minor version of promotion to GA
+- [ ] (R) Production readiness review completed
+- [ ] (R) Production readiness review approved
+- [ ] "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
+
+<!--
+**Note:** This checklist is iterative and should be reviewed and updated every time this enhancement is being considered for a milestone.
+-->
+
+[kubernetes.io]: https://kubernetes.io/
+[kubernetes/enhancements]: https://git.k8s.io/enhancements
+[kubernetes/kubernetes]: https://git.k8s.io/kubernetes
+[kubernetes/website]: https://git.k8s.io/website
+
+## Summary
+
+This KEP proposes a cache which reuses the node feasibility and scoring from
+earlier pods to reduce the cost of scheduling follow on pods that have the same constraints. 
+To simplify the first version, we only attempt this optimization for pods that have a single pod 
+of this type on a node, and we exclude pods that use complex constraints like
+PodTopologySpread and PodAffinity / AntiAffinity. We also have tight
+eviction rules on the cache (O(seconds)) to ensure it doesn't get stale. To tell if
+two pods are the "same" we introduce a signature API to plugins.
+
+## Motivation
+
+Today our scheduling algorithm is p x n where p is the number of pods to schedule
+and n the number of nodes in the cluster. As the size of clusters and jobs continue
+to increase, this leads to low performance when scheduling or rescheduling 
+large jobs. This increases customer cost and slows down customer jobs, both
+unpleasant impacts. Optimizations like this one have the potential to dramaticly
+reduce the cost of scheduling in these scenarios.
+
+We are also working on gang scheduling, which will give us a way to consider multiple 
+pods at the same time. While full gang scheduling will likely deprecate the cache, 
+the signatures and reuse work should be portable to the new cycles that consider multiple pods.
+
+Another change is the shift towards 1-pod-per-node in batch and ML environments. Many
+of these environments (among others) only attempt to run a single customer pod on each node, along
+with a complement of daemon set pods. This simplifies our scheduling needs significantly.
+
+### Goals
+
+ * Improve the performance of scheduling large jobs on large clusters where the constraints are simple.
+ * Begin building infrastructure to support gang scheduling and other "multi-pod" scheduling requests.
+ * Ensure that the infrastructure we build is maintainable as we update, add and remove plugins.
+ * Never impact feasibility.
+ * Provide identical results to our current scheduler for 1-pod-per-node environments.
+ * Provide an experience with limited changes in multi-pod-per-node environments.
+
+### Non-Goals
+
+ * We are not attempting to apply this optimization to all pods. Some constraints
+do not lend themselves to caching. Instead, we focus on customers who have
+large jobs, who need high performance, and do not generally use these
+more complex constraints. While we expect the cache to always be on, many pods will not be 
+able to take advantage of it.
+ * We are not adding gang scheduling of any kind in this KEP. This is purely a performance
+improvement, although we hope the work on this KEP will help us with gang scheduling as we build it.
+
+## Proposal
+
+See https://github.com/bwsalmon/kubernetes/pull/1 for a draft version of the code. Feedback is extremely welcome.
+
+We introduce two things:
+ * The pod cache.
+ * A pod scheduling signature
+
+### Pod cache
+
+The end result of the scheduler cycles is a list of node names that will fit
+the current pod, ordered by their score from most to least attractive. This result
+is what we cache; the current pod will use the first node on the list, and we cache the remaining
+results, indexed by the pod's "scheduling signature" (which we will describe later).
+
+When a pod with the same signature comes later, we find the entry in our cache and pull the first
+node off the list. We then go down the nominated node path. Just as we would with a pod
+with a NominatedNodeName in it, we only re-evaluate the feasibility of this node before using it.
+
+Since we assume 1-pod-per-node, we know that the node used by the current pod 
+cannot be used by subsequent pods (of any signature).
+Thus we remove the host from all signatures in the cache. The cache is built
+in a way that makes it easy to remove entries by either pod signature or host
+so we can efficiently invalide entries. If we are not in a 1-pod-per-node
+we could get "sub-optimal" results if the node just used is the best node for
+some other pod, but this should be the only issue.
+
+All stored results are timestamped, and we remove entries when they are more
+than a few seconds old to ensure we do not get stale data. Since we are targeting
+large numbers of pods of the same type, this should be sufficient to get the benefit
+we are looking for.
+
+### Pod scheduling signature
+
+The pod scheduling signature is used to determine if two pods are "the same"
+from a scheduling perspective. In specific, what this means is that any pod
+with the given signature will get the same scores / feasibility results from
+any arbitrary set of nodes. This is necessary for the cache to work, since we
+need to be able to reuse the previous work.
+
+Note that some pods will not have a signature, because the scoring uses not
+just pod and node attributes, but other pods in the system, global data
+about pod placement, etc. These nodes get a nil signature, and we fallback
+to the slow path.
+
+We exclude pods with the following constraints:
+   * Pod affinity rules (affinity or anti-affinity)
+   * Topology spread rules (including inherited rules from the system default) This constraint we should attempt to lift in the future.
+
+To construct a signature, we add a new function for each plugin to implement.
+This function takes a pod and generates a signature for that plugin as a string. 
+The signature is likely a set of attributes of the pod, or something derived from them. 
+To construct a full signature we take the signatures of all the plugins and aggeregate them into
+a single string. If any plugin cannot generate a signature for a given pod (because it depends on information other
+than the pod and node), then we generate a "nil" signature and don't attemp to cache the pod.
+
+Initially we won't require plugins to implement the new function, but we will turn off signatures for
+all pods if a plugin is enabled that does not implement it. In subsequent releases we will
+make implementation of the function a requirement, but of course plugins are also able to
+say pods are unsignable.
+
+### Notes/Constraints/Caveats (Optional)
+
+### Risks and Mitigations
+
+<!--
+What are the risks of this proposal, and how do we mitigate? Think broadly.
+For example, consider both security and how this will impact the larger
+Kubernetes ecosystem.
+
+How will security be reviewed, and by whom?
+
+How will UX be reviewed, and by whom?
+
+Consider including folks who also work outside the SIG or subproject.
+-->
+
+## Design Details
+
+### Pod signature v1
+
+The follow section outlines the attributes we are currently proposing to use as the signature for each of the 
+plugins in the scheduler. We need the plugin owners to validate that these signatures are correct, or help
+us find the correct signature.
+
+Note that the signature does not need to be stable across versions, or even invocations of the scheduler. 
+It only needs to be comparable between pods on a given running scheduler instance.
+
+ * DynamicResources: For now we mark a pod unsignable if it has dynamic resource claims. We should improve this in the future, since most
+ resource claims will allow for a signature.
+ * ImageLocality: We use the canonicalized image names from the Volumes as the signature.
+ * InterPodAffinity: If either the PodAffinity or PodAntiAffinity fields are set, the pod is marked unsignable, otherwise an empty signature.
+ * NodeAffinity: We use the NodeAffinity and NodeSelector fields as the signature.
+ * NodeName: We use the NodeName field as the signature.
+ * NodePorts: We use the results from util.GetHostPorts(pod) as the signature.
+ * NodeResourcesBalancedAllocation: We use the output of calculatePodResourceRequestList as the signature.
+ * NodeResourcesFit: We use the output of the computePodResourceRequest function as the signature.
+ * NodeUnschedulable: We use the Tolerations field as the signature.
+ * NodeVolumeLimits: We use all Volume information except from Volumes of type ConfigMap or Secret.
+ * PodTopologySpread: If the PodTopologySpead field is set, or it is not set but a default set of rules are applied, we mark the pod unsignable, otherwise it returns an empty signature.
+ * TaintToleration: We use the Tolerations field as the signature.
+ * VolumeBinding: Same as NodeVolumeLimits.
+ * VolumeRestrictions: Same as NodeVolumeLimits.
+ * VolumeZone: Same as NodeVolumeLimits.
+
+### Test Plan
+
+<!--
+**Note:** *Not required until targeted at a release.*
+The goal is to ensure that we don't accept enhancements with inadequate testing.
+
+All code is expected to have adequate tests (eventually with coverage
+expectations). Please adhere to the [Kubernetes testing guidelines][testing-guidelines]
+when drafting this test plan.
+
+[testing-guidelines]: https://git.k8s.io/community/contributors/devel/sig-testing/testing.md
+-->
+
+[ ] 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.
+
+##### Prerequisite testing updates
+
+<!--
+Based on reviewers feedback describe what additional tests need to be added prior
+implementing this enhancement to ensure the enhancements have also solid foundations.
+-->
+
+##### Unit tests
+
+<!--
+In principle every added code should have complete unit test coverage, so providing
+the exact set of tests will not bring additional value.
+However, if complete unit test coverage is not possible, explain the reason of it
+together with explanation why this is acceptable.
+-->
+
+<!--
+Additionally, for Alpha try to enumerate the core package you will be touching
+to implement this enhancement and provide the current unit coverage for those
+in the form of:
+- <package>: <date> - <current test coverage>
+The data can be easily read from:
+https://testgrid.k8s.io/sig-testing-canaries#ci-kubernetes-coverage-unit
+
+This can inform certain test coverage improvements that we want to do before
+extending the production code to implement this enhancement.
+-->
+
+- `<package>`: `<date>` - `<test coverage>`
+
+##### Integration tests
+
+<!--
+Integration tests are contained in https://git.k8s.io/kubernetes/test/integration.
+Integration tests allow control of the configuration parameters used to start the binaries under test.
+This is different from e2e tests which do not allow configuration of parameters.
+Doing this allows testing non-default options and multiple different and potentially conflicting command line options.
+For more details, see https://github.com/kubernetes/community/blob/master/contributors/devel/sig-testing/testing-strategy.md
+
+If integration tests are not necessary or useful, explain why.
+-->
+
+<!--
+This question should be filled when targeting a release.
+For Alpha, describe what tests will be added to ensure proper quality of the enhancement.
+
+For Beta and GA, document that tests have been written,
+have been executed regularly, and have been stable.
+This can be done with:
+- permalinks to the GitHub source code
+- links to the periodic job (typically https://testgrid.k8s.io/sig-release-master-blocking#integration-master), filtered by the test name
+- a search in the Kubernetes bug triage tool (https://storage.googleapis.com/k8s-triage/index.html)
+-->
+
+- [test name](https://github.com/kubernetes/kubernetes/blob/2334b8469e1983c525c0c6382125710093a25883/test/integration/...): [integration master](https://testgrid.k8s.io/sig-release-master-blocking#integration-master?include-filter-by-regex=MyCoolFeature), [triage search](https://storage.googleapis.com/k8s-triage/index.html?test=MyCoolFeature)
+
+##### e2e tests
+
+<!--
+This question should be filled when targeting a release.
+For Alpha, describe what tests will be added to ensure proper quality of the enhancement.
+
+For Beta and GA, document that tests have been written,
+have been executed regularly, and have been stable.
+This can be done with:
+- 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.
+-->
+
+- [test name](https://github.com/kubernetes/kubernetes/blob/2334b8469e1983c525c0c6382125710093a25883/test/e2e/...): [SIG ...](https://testgrid.k8s.io/sig-...?include-filter-by-regex=MyCoolFeature), [triage search](https://storage.googleapis.com/k8s-triage/index.html?test=MyCoolFeature)
+
+### 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
+-->
+
+### 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?
+-->
+
+### 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.
+-->
+
+## 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/
+-->
+
+- [ ] Feature gate (also fill in values in `kep.yaml`)
+  - Feature gate name:
+  - Components depending on the feature gate:
+- [ ] Other
+  - Describe the mechanism:
+  - Will enabling / disabling the feature require downtime of the control
+    plane?
+  - Will enabling / disabling the feature require downtime or reprovisioning
+    of a node?
+
+###### 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.
+-->
+
+###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?
+
+<!--
+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?
+
+###### Are there any tests for feature enablement/disablement?
+
+<!--
+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?
+
+<!--
+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?
+
+<!--
+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?
+
+<!--
+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.?
+
+<!--
+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?
+
+<!--
+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?
+
+<!--
+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.
+-->
+
+- [ ] Events
+  - Event Reason: 
+- [ ] API .status
+  - Condition name: 
+  - Other field: 
+- [ ] Other (treat as last resort)
+  - Details:
+
+###### 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.
+-->
+
+###### 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.
+-->
+
+- [ ] Metrics
+  - Metric name:
+  - [Optional] Aggregation method:
+  - Components exposing the metric:
+- [ ] Other (treat as last resort)
+  - Details:
+
+###### 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.).
+-->
+
+### Dependencies
+
+<!--
+This section must be completed when targeting beta to a release.
+-->
+
+###### Does this feature depend on any specific services running in the cluster?
+
+<!--
+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?
+
+<!--
+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?
+
+<!--
+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?
+
+<!--
+Describe them, providing:
+  - Which API(s):
+  - Estimated increase:
+-->
+
+###### 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)
+-->
+
+###### 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
+-->
+
+###### 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
+-->
+
+###### 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?
+-->
+
+### 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?
+
+###### 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.
+-->
+
+###### What steps should be taken if SLOs are not being met to determine the problem?
+
+## Implementation History
+
+<!--
+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
+
+<!--
+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.
+-->
+
+## 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.
+-->
diff --git a/keps/sig-scheduling/5598-scheduling-cache/kep.yaml b/keps/sig-scheduling/5598-scheduling-cache/kep.yaml
new file mode 100644
index 00000000000..46cea616b75
--- /dev/null
+++ b/keps/sig-scheduling/5598-scheduling-cache/kep.yaml
@@ -0,0 +1,43 @@
+title: Scheduling cache
+kep-number: 5598
+authors:
+  - "@bsalmon@google.com"
+owning-sig: sig-scheduling
+participating-sigs:
+status: provisional
+creation-date: 2025-10-01
+reviewers:
+  - TBD
+  - "@alice.doe"
+approvers:
+  - TBD
+  - "@oscar.doe"
+
+# The target maturity stage in the current dev cycle for this KEP.
+# If the purpose of this KEP is to deprecate a user-visible feature
+# and a Deprecated feature gates are added, they should be deprecated|disabled|removed.
+stage: alpha|beta|stable
+
+# The most recent milestone for which work toward delivery of this KEP has been
+# done. This can be the current (upcoming) milestone, if it is being actively
+# worked on.
+latest-milestone: "v1.35"
+
+# The milestone at which this feature was, or is targeted to be, at each stage.
+milestone:
+  alpha: "v1.35"
+  beta: "v1.36"
+  stable: "v1.37"
+
+# The following PRR answers are required at alpha release
+# List the feature gate name and the components for which it must be enabled
+feature-gates:
+  - name: MyFeature
+    components:
+      - kube-apiserver
+      - kube-controller-manager
+disable-supported: true
+
+# The following PRR answers are required at beta release
+metrics:
+  - my_feature_metric

From 68a3857c5f7c4eefbf03f469e1b1bae2a1e985c9 Mon Sep 17 00:00:00 2001
From: Brandon Salmon <bsalmon@google.com>
Date: Fri, 3 Oct 2025 19:46:45 +0000
Subject: [PATCH 2/5] Add section on eCache.

---
 .../5598-scheduling-cache/README.md           | 59 +++++++++++++++++++
 1 file changed, 59 insertions(+)

diff --git a/keps/sig-scheduling/5598-scheduling-cache/README.md b/keps/sig-scheduling/5598-scheduling-cache/README.md
index 817a48a75fd..1c282bffb80 100644
--- a/keps/sig-scheduling/5598-scheduling-cache/README.md
+++ b/keps/sig-scheduling/5598-scheduling-cache/README.md
@@ -266,6 +266,65 @@ all pods if a plugin is enabled that does not implement it. In subsequent releas
 make implementation of the function a requirement, but of course plugins are also able to
 say pods are unsignable.
 
+### Comparison with Equivalence Cache (circa 2018)
+
+This KEP is addressing a very similar problem to the Equivalence Cache (eCache), an approach 
+suggested in 2018 and then retracted because it became extremely complex. While this KEP addresses a similar problem
+it does so in a very different way, which we believe avoids the issues experienced by the eCache
+
+The two issues experienced by eCache were:
+
+ * eCache performance was still O(num nodes).
+ * eCache was complex
+ * eCache was tightly coupled with plugins.
+
+ We'll address each in turn, but at a high level the differences stem from our scope reduction in this cache, where
+ we focus on simple constraints in a 1-pod-per-node world, and are comfortable extending our "race" period slightly.
+
+ #### eCache performance was still O(num nodes)
+
+ The eCache was caching a fundamentally different result than this cache. In the case of the eCache they were caching
+ the results of a predicate p, (which is sounds like was one of a number of ops for a given plugin) for a specific pod and node.
+ This meant the number of cache lookups per pod was O(num nodes * num predicates) where num predicates was O(num plugins). Because 
+ the cache was so fine-grained, the cache lookups were, in many cases, more expensive than the actual computation. This also meant
+ that while the cache could improve performance, it fundamentally did not remove the O(num nodes) nature of the per pod computation.
+
+ In the case of this cache, we are looking up the entire host filtering and scoring for a single pod, so the number of cache lookups
+ per pod is 1. We are caching the entire filtering / scoring result, so the map lookup is guaranteed to be faster even
+ than just iterating over the plugins themselves, let alone the computation needed to filter / score. As the number of nodes go up,
+ the fact that the cache lookup is O(1) per pod will make it an increasingly perfromant alternative to the full computation.
+
+ We can cache this more granular data because we only cache for simple plugins, and in fact avoid the complex plugins entirely.
+ Thus we do not need to be concerned about cross pod dependencies, meaning we do not need to try to keep detailed information 
+ up-to-date. Because we assume 1-pod-per-node and some amount of "staleness" we simply need to invalidate whole hosts, rather 
+ than requiring upkeep of complex predicate results required to keep the eCache functional.
+
+ #### eCache was complex
+
+ Because the eCache cached predicates, the logic for computing these results went into the cache as well. This meant that significant 
+ amount of the plugin functionality were replicated in the cache layer. This added significant complexity to the cache, and also made 
+ keeping the cache results themselves up to date was complex, involving multiple pods, etc. Because the eCache only improved performance for complex queries, they needed to include this complexity to provide value.
+
+ In contrast, the signature used in this cache is just a subset of the pod object, without complex logic. It is static and as the pod object changes slowly, it will change slowly as well. In addition, we explicitly avoid all the complex predicates in this cache because they are rarely used. Thus we do not have the same complexity needed in the cache.
+ 
+ ### The eCache was tightly coupled with plugins
+ 
+ Beacuse a significant amount of the plugin complexity made into the eCache, it was difficult for plugin owners to keep the things in sync.
+ As the pod object is fairly stable, this makes keeping the signature up to date a much simpler task. The creation of the signature is 
+ also spread across the plugins themselves, so instead of needing to keep the cache up to date, plugin owners simply have a new function 
+ they need to manage within their plugin, which the cache simply aggregates.
+
+ We will also need to provide tests that evaluate different pod configurations against different node configurations and ensures that
+ any time the signatures match the results do as well. This will help us catch issues in the future, in addition to providing
+ testing opportunities in other areas.
+
+ If plugin changes prove to be an issue, we could codify the signature as a new "Scheduling" object that only has a subset
+ of the fields of the pod. Plugins that "opt-in" could only be given access to this reduced scheduling object, and we could then use the entire scheduling object as the signature. This would make it more or less impossible for the signature and plugins to be out of sync, and would
+ naturally surface new dependencies as additions to the scheduling object. However, as we expect plugin changes to be relatively 
+ modest, we don't believe the complexity of making the interface changes is worth the risk today.
+
+See https://github.com/kubernetes/kubernetes/pull/65714#issuecomment-410016382 as starting point on eCache.
+
 ### Notes/Constraints/Caveats (Optional)
 
 ### Risks and Mitigations

From 25189274cc83421e05bc1d889d141450ff37d21b Mon Sep 17 00:00:00 2001
From: Brandon Salmon <bsalmon@google.com>
Date: Fri, 3 Oct 2025 19:50:36 +0000
Subject: [PATCH 3/5] Wording clarifications.

---
 keps/sig-scheduling/5598-scheduling-cache/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/keps/sig-scheduling/5598-scheduling-cache/README.md b/keps/sig-scheduling/5598-scheduling-cache/README.md
index 1c282bffb80..125a04814c3 100644
--- a/keps/sig-scheduling/5598-scheduling-cache/README.md
+++ b/keps/sig-scheduling/5598-scheduling-cache/README.md
@@ -310,11 +310,11 @@ The two issues experienced by eCache were:
  ### The eCache was tightly coupled with plugins
  
  Beacuse a significant amount of the plugin complexity made into the eCache, it was difficult for plugin owners to keep the things in sync.
- As the pod object is fairly stable, this makes keeping the signature up to date a much simpler task. The creation of the signature is 
- also spread across the plugins themselves, so instead of needing to keep the cache up to date, plugin owners simply have a new function 
- they need to manage within their plugin, which the cache simply aggregates.
+ Since in this cache the signature is just parts of the pod object, and the pod object is fairly stable, this makes keeping the signature up
+ to date a much simpler task. The creation of the signature is also spread across the plugins themselves, so instead of needing to keep the 
+ cache up to date, plugin owners simply have a new function they need to manage within their plugin, which the cache simply aggregates.
 
- We will also need to provide tests that evaluate different pod configurations against different node configurations and ensures that
+ We will also provide tests that evaluate different pod configurations against different node configurations and ensures that
  any time the signatures match the results do as well. This will help us catch issues in the future, in addition to providing
  testing opportunities in other areas.
 

From 47bc765fbf3060974860538c6d4b0be83fc6b16f Mon Sep 17 00:00:00 2001
From: Brandon Salmon <bsalmon@google.com>
Date: Fri, 3 Oct 2025 19:52:44 +0000
Subject: [PATCH 4/5] More cleanup.

---
 keps/sig-scheduling/5598-scheduling-cache/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/keps/sig-scheduling/5598-scheduling-cache/README.md b/keps/sig-scheduling/5598-scheduling-cache/README.md
index 125a04814c3..5d9e17b62bf 100644
--- a/keps/sig-scheduling/5598-scheduling-cache/README.md
+++ b/keps/sig-scheduling/5598-scheduling-cache/README.md
@@ -303,11 +303,11 @@ The two issues experienced by eCache were:
 
  Because the eCache cached predicates, the logic for computing these results went into the cache as well. This meant that significant 
  amount of the plugin functionality were replicated in the cache layer. This added significant complexity to the cache, and also made 
- keeping the cache results themselves up to date was complex, involving multiple pods, etc. Because the eCache only improved performance for complex queries, they needed to include this complexity to provide value.
+ keeping the cache results themselves up to date complex, involving multiple pods, etc. Because the eCache only improved performance for complex queries, they needed to include this complexity to provide value.
 
  In contrast, the signature used in this cache is just a subset of the pod object, without complex logic. It is static and as the pod object changes slowly, it will change slowly as well. In addition, we explicitly avoid all the complex predicates in this cache because they are rarely used. Thus we do not have the same complexity needed in the cache.
  
- ### The eCache was tightly coupled with plugins
+ #### eCache was tightly coupled with plugins
  
  Beacuse a significant amount of the plugin complexity made into the eCache, it was difficult for plugin owners to keep the things in sync.
  Since in this cache the signature is just parts of the pod object, and the pod object is fairly stable, this makes keeping the signature up

From b8330020ee3b2e517ad79a821e4bb4abe1abfd01 Mon Sep 17 00:00:00 2001
From: Brandon Salmon <bsalmon@google.com>
Date: Mon, 6 Oct 2025 13:48:51 +0000
Subject: [PATCH 5/5] Cleanup and add comparison to ecache.

---
 .../5598-scheduling-cache/README.md           | 50 +++++++++++--------
 1 file changed, 30 insertions(+), 20 deletions(-)

diff --git a/keps/sig-scheduling/5598-scheduling-cache/README.md b/keps/sig-scheduling/5598-scheduling-cache/README.md
index 5d9e17b62bf..317c84cc992 100644
--- a/keps/sig-scheduling/5598-scheduling-cache/README.md
+++ b/keps/sig-scheduling/5598-scheduling-cache/README.md
@@ -171,16 +171,17 @@ two pods are the "same" we introduce a signature API to plugins.
 
 ## Motivation
 
-Today our scheduling algorithm is p x n where p is the number of pods to schedule
-and n the number of nodes in the cluster. As the size of clusters and jobs continue
+Today our scheduling algorithm is O(num pods x num nodes) where num pods is the number of pods to schedule
+and num nodes the number of nodes in the cluster. As the size of clusters and jobs continue
 to increase, this leads to low performance when scheduling or rescheduling 
 large jobs. This increases customer cost and slows down customer jobs, both
 unpleasant impacts. Optimizations like this one have the potential to dramaticly
 reduce the cost of scheduling in these scenarios.
 
-We are also working on gang scheduling, which will give us a way to consider multiple 
-pods at the same time. While full gang scheduling will likely deprecate the cache, 
-the signatures and reuse work should be portable to the new cycles that consider multiple pods.
+We are also working on multi-pod scheduling, which will give us a way to consider multiple 
+pods at the same time. While full multi-pod scheduling will likely deprecate the cache, 
+the signatures work and structuring of the cache dependencies should be portable to the new cycles
+that consider multiple pods.
 
 Another change is the shift towards 1-pod-per-node in batch and ML environments. Many
 of these environments (among others) only attempt to run a single customer pod on each node, along
@@ -272,7 +273,7 @@ This KEP is addressing a very similar problem to the Equivalence Cache (eCache),
 suggested in 2018 and then retracted because it became extremely complex. While this KEP addresses a similar problem
 it does so in a very different way, which we believe avoids the issues experienced by the eCache
 
-The two issues experienced by eCache were:
+The issues experienced by eCache were:
 
  * eCache performance was still O(num nodes).
  * eCache was complex
@@ -302,19 +303,20 @@ The two issues experienced by eCache were:
  #### eCache was complex
 
  Because the eCache cached predicates, the logic for computing these results went into the cache as well. This meant that significant 
- amount of the plugin functionality were replicated in the cache layer. This added significant complexity to the cache, and also made 
- keeping the cache results themselves up to date complex, involving multiple pods, etc. Because the eCache only improved performance for complex queries, they needed to include this complexity to provide value.
+ amount of the plugin functionality was replicated in the cache layer. This added significant complexity to the cache, and also made 
+ keeping the cache results themselves up to date complex, involving multiple pods, etc. Because the eCache only improved performance 
+ for complex queries, it needed to include this complexity to provide value.
 
- In contrast, the signature used in this cache is just a subset of the pod object, without complex logic. It is static and as the pod object changes slowly, it will change slowly as well. In addition, we explicitly avoid all the complex predicates in this cache because they are rarely used. Thus we do not have the same complexity needed in the cache.
+ In contrast, the signature used in this cache is just a subset of the pod object, without complex logic. It is static and as the pod object changes slowly, it will change slowly as well. In addition, we explicitly avoid all the complex plugins in this cache because they are rarely used. Thus we do not have the same complexity needed in the cache.
  
  #### eCache was tightly coupled with plugins
  
- Beacuse a significant amount of the plugin complexity made into the eCache, it was difficult for plugin owners to keep the things in sync.
+ Because a significant amount of the plugin complexity made into the eCache, it was difficult for plugin owners to keep the things in sync.
  Since in this cache the signature is just parts of the pod object, and the pod object is fairly stable, this makes keeping the signature up
  to date a much simpler task. The creation of the signature is also spread across the plugins themselves, so instead of needing to keep the 
- cache up to date, plugin owners simply have a new function they need to manage within their plugin, which the cache simply aggregates.
+ cache up to date, plugin owners simply have a new function they need to manage within their plugin, which the cache only aggregates.
 
- We will also provide tests that evaluate different pod configurations against different node configurations and ensures that
+ We will also provide tests that evaluate different pod configurations against different node configurations and ensure that
  any time the signatures match the results do as well. This will help us catch issues in the future, in addition to providing
  testing opportunities in other areas.
 
@@ -329,17 +331,25 @@ See https://github.com/kubernetes/kubernetes/pull/65714#issuecomment-410016382 a
 
 ### Risks and Mitigations
 
-<!--
-What are the risks of this proposal, and how do we mitigate? Think broadly.
-For example, consider both security and how this will impact the larger
-Kubernetes ecosystem.
+#### Plugins need to keep signatures up to date
 
-How will security be reviewed, and by whom?
+The cache will only work if plugin maintainers are able to keep their portion of the signature up-to-date. We beleive this should
+be doable because the logic is put into the plugin interface itself, and we are restricting it to portions of the pod spec,
+but there is still risk of subtle dependencies creeping in.
 
-How will UX be reviewed, and by whom?
+If plugin changes prove to be an issue, we could codify the signature as a new "Scheduling" object that only has a subset
+of the fields of the pod. Plugins that "opt-in" could only be given access to this reduced scheduling object, and we could then use the entire scheduling object as the signature. This would make it more or less impossible for the signature and plugins to be out of sync, and would
+naturally surface new dependencies as additions to the scheduling object. However, as we expect plugin changes to be relatively 
+modest, we don't believe the complexity of making the interface changes is worth the risk today.
 
-Consider including folks who also work outside the SIG or subproject.
--->
+#### We are narrowing the feature set where the cache will work
+
+Because we are explicitly limiting the functionality that this cache will support, we run the risk of designing something
+that will not work for enough customers for it to be useful.
+
+To mitigate this risk we are actively engaging with customers and doing analysis of data available on K8s users to ensure we are still
+capturing a large enough number of user use cases. We also will address this by expanding over time; we expect to have a few interested
+parties up front, but will then evaluate expansions that could onboard more.
 
 ## Design Details