From bed72f0389d95fa9065370acdeadf9ea497722b7 Mon Sep 17 00:00:00 2001 From: Zeel Patel Date: Wed, 23 Oct 2024 13:46:22 +0530 Subject: [PATCH] KEP - Add a Filter plugin to ensure that non-GPU pods are not scheduled on GPU nodes --- kep/tbd-gpu-aware-scheduling/README.md | 408 ++++++++++++++++++ .../figs/filter_plugin_flow.png | Bin 0 -> 414713 bytes .../figs/plugin_for_daemonset_pod.png | Bin 0 -> 118979 bytes kep/tbd-gpu-aware-scheduling/kep.yaml | 13 + 4 files changed, 421 insertions(+) create mode 100644 kep/tbd-gpu-aware-scheduling/README.md create mode 100644 kep/tbd-gpu-aware-scheduling/figs/filter_plugin_flow.png create mode 100644 kep/tbd-gpu-aware-scheduling/figs/plugin_for_daemonset_pod.png create mode 100644 kep/tbd-gpu-aware-scheduling/kep.yaml diff --git a/kep/tbd-gpu-aware-scheduling/README.md b/kep/tbd-gpu-aware-scheduling/README.md new file mode 100644 index 000000000..20197866c --- /dev/null +++ b/kep/tbd-gpu-aware-scheduling/README.md @@ -0,0 +1,408 @@ + +# KEP-NNNN: GPU Aware Scheduling + + + + + + +- [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) + + +## Release Signoff Checklist + + + +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) +- [ ] (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 + + + +[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 +A pod not requesting GPUs can run and get scheduled on node advertising GPUs as a GPU node also has CPU, memory and +disk, and end up keeping node busy. This can lead to inefficient utilization of GPUs in kubernetes cluster. The Filter +plugin will not allow non-GPU pods to run on GPU nodes. + +## Motivation +In some of the kubernetes workloads, there can be both CPU requesting pods and CPU+GPU requesting pods, +and it is a fairly common paradigm. So having hybrid kubernetes clusters (having both GPU and +non-GPU kubelet nodes) is required. As kubernetes scheduler can schedule non-GPU workloads on GPU nodes, we +can run into the issue of CPU only workloads occupying GPU nodes. GPU nodes also have CPU and Memory and can +potentially run CPU only workloads. This can result in GPU workloads unnecessarily waiting as they cannot run +on non-GPU nodes. This document explains Kubernetes Filter Plugin to be implemented to deal with above mentioned +shortcomings. + +### Goals +- The Filter plugin should not allow non-GPU pods on GPU nodes. +- The Filter plugin should not affect any other scheduling decision made by the kubernetes scheduler. + +### Non-Goals +- The added Filter plugin is not responsible for any scheduling decision made to schedule GPU pods on GPU nodes +and non-GPU pods on non-GPU nodes. + + +## Proposal + + + +#### Story 1 + +#### Story 2 + +### Risks and Mitigations + + + +## Design Details +To prevent non-GPU pods from getting scheduled on GPU nodes, we plan to add a new filter +and prefilter plugins at Filter and PreFilter extension points respectively. + +![PEAKS workflow](./figs/filter_plugin_flow.png) + +#### PreFilter State +Every plugin in kubernetes scheduler keeps a CycleState to store and retrieve arbitrary data. +CycleState uses a sync.Map to back the storage. It's aimed to optimize for the write once and +read many times scenarios. It is the recommended pattern used in all in-tree plugins. +Plugin-specific state is written once in PreFilter/PreScore and afterwards read many times in Filter/Score. + +In our case PreFilter will check if a given pod is requesting GPUs or not by checking resource +limits and will update the PreFilter State accordingly. Pod requesting non-zero GPU +in limits will be considered as GPU requesting pod. Checking pod limit because as per [kuberetes +documentations](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/#using-device-plugins), +- You can specify resource limits without specifying requests because Kubernetes will use the limit as the request value by default. +- You can specify the resource in both limits and requests but these two values must be equal. +- You cannot specify resource requests without specifying limits. + +#### Filter State +For a given pod resource, Filter plugin will check all the nodes in the cluster. It will check +whether the node is GPU node or not by checking node’s Allocatable field. If a node has an allocatable +GPU resource, it will consider that node as GPU node. 'Allocatable' on a Kubernetes +node is defined as the amount of compute resources that are available for pods. Note that node +resources which have been allocated to pods running on that node will not be subtracted from the +node allocatable resources. + +Filter will read the cycle state stored by the PreFilter plugin. If the pod is not requesting +GPUs and a given node is a GPU node, Filter plugin will mark that pod as unschedulable for that +node and for all the other cases, Filter plugin will mark pod as schedulable. + +##### Case to be handled during device plugin upgrade process +GPUs in kubernetes nodes are advertised using device plugins. And assuming that this device plugin in is running as +daemonset, GPUs on nodes are advertised using daemonset pods. + +The Filter plugin will also take GPU daemonset pods into consideration. GPU daemonset is responsible for showing +GPU information on nodes. Without GPU daemonset, node will not show GPU capacity on node describe. +When the GPU plugin daemonset spec is upgraded, daemonset pods are upgraded due to a new rollout of the +daemonset spec. Old daemonset pod is removed and a new daemonset pod needs to be placed. + +We are assuming that GPU daemonset pods run on nodes with GPU labels (they have a label selector field set). But the +daemonset pod does not request any GPUs. Because of the filter plugin explained above, daemonset pods can get stuck in +pending state forever. + +So, the scheduler plugin will exclude the pods which contain the GPU device-plugin container image from filtering +logic. + +![PEAKS workflow](./figs/plugin_for_daemonset_pod.png) + +The overall flow will look something like this. CycleState in the filter plugin will store whether the pod is requesting +GPUs or not. And whether the pod is an GPU daemonset pod or not. + +For every pod created, at the PreFilter state, the plugin will update the CycleState for that pod by looking at +all the containers of the pod. Container requests to check GPU/non-GPU pods and container images to check GPU +device plugin pod or not. + +At the Filter state, the plugin will read the CycleState stored at the PreFilter state. For a given pod, it will +check all the nodes. It marks the non GPU device plugin pods not requesting GPUs unschedulable on GPU nodes. + + +### Test Plan + +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 + +##### Unit tests +- Add detailed unit tests with 100% coverage. +- ``: `` - `` + +##### Integration tests +- TBD +- : + +##### e2e tests +- TBD +- : + +## Production Readiness Review Questionnaire + +### Feature Enablement and Rollback + +###### How can this feature be enabled / disabled in a live cluster? +By enabling the filter and pre-filter plugin in the k8s scheduler config. + +###### Does enabling the feature change any default behavior? +No + +###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)? +Yes, disable the plugin from scheduler config. + +###### What happens if we reenable the feature if it was previously rolled back? + +###### Are there any tests for feature enablement/disablement? +N/A + +### Rollout, Upgrade and Rollback Planning +N/A + +###### How can a rollout or rollback fail? Can it impact already running workloads? +N/A + +###### What specific metrics should inform a rollback? +N/A + +###### Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested? +N/A + +###### Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.? +No + +### Monitoring Requirements + +###### How can an operator determine if the feature is in use by workloads? +N/A + +###### How can someone using this feature know that it is working for their instance? +By checking non-GPU pods are not getting scheduled on GPU nodes. + +###### What are the reasonable SLOs (Service Level Objectives) for the enhancement? +N/A + +###### What are the SLIs (Service Level Indicators) an operator can use to determine the health of the service? +N/A + +###### Are there any missing metrics that would be useful to have to improve observability of this feature? +No + +### Dependencies + +###### Does this feature depend on any specific services running in the cluster? +It expects kubernetes scheduler to be running. + +### Scalability + +###### Will enabling / using this feature result in any new API calls? +No + +###### Will enabling / using this feature result in introducing new API types? +No + +###### Will enabling / using this feature result in any new calls to the cloud provider? +No + +###### Will enabling / using this feature result in increasing size or count of the existing API objects? +No + +###### Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs? +No + +###### Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components? +No + +###### Can enabling / using this feature result in resource exhaustion of some node resources (PIDs, sockets, inodes, etc.)? +No + +### Troubleshooting + +###### How does this feature react if the API server and/or etcd is unavailable? + +###### What are other known failure modes? + +###### What steps should be taken if SLOs are not being met to determine the problem? + +## Implementation History + +## Drawbacks + +## Alternatives + +Taint all GPU nodes at the beginning with a No Schedule taint, this will ensure no jobs will get scheduled on the nodes +with taint. + +```yaml +kubectl taint nodes node1 gpuNodeKey=GPUNodeValue:NoSchedule +``` + +Write a mutating admission controller, to add a toleration to the pod spec of any workload that requests for GPUs, +or use the ExtendedResourceToleration Admission controller + +```yaml +tolerations: +- key: "gpuNodeKey" + operator: "Equal" + value: "GPUNodeValue" + effect: "NoSchedule" +``` + +Since the GPU pods would have tolerations they would run on the tainted GPU nodes. +CPU pods on the other hand will not run on GPU nodes as they won’t have any tolerations. + +The drawback in this approach is that, either pod creator(client) needs to add tolerations or it needs to be added by +kubernetes cluster manager through mutating admission controller without client's knowledge. +Taints are on nodes and tolerations are on pods. Submitter of pods and managers of nodes are two different teams sitting +in different orgs. So one can not manage both the taints and tolerations. And it will increase the chances of errors. + + +## Infrastructure Needed (Optional) + diff --git a/kep/tbd-gpu-aware-scheduling/figs/filter_plugin_flow.png b/kep/tbd-gpu-aware-scheduling/figs/filter_plugin_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..56d1039e1e1d62b8c15ac0f531af3b8bd9643cea GIT binary patch literal 414713 zcmeFZX*iT`{5~v7lwBAMvZsW}$WGZM6iu0|W9+i;`5EOXeA5RcAcg2@YY2ouKYf|@{cELZmGj$Y%<9}Xv5*xot-u$0`;*{7k!v4Lw zuI0bgRQUHYd9B^W``?dMeBoQ9rT+JV&)?V$|L-%!_ScU8`-sAGvDe=JeT3sqs+`P! zABmyU_}^vzchmmY(*6&>>3%(P3Gk1Yn(GXnS4}#G-6jCnakSLB@VaJXKC!ErH=HN^ zXh>Nz=kr&|yLXW2F1js=uI1)Mkl~q7qfCN9No9mw_Ycgd^^_71JyN^_MuyH zhk}%y3fWFD_OfqkM#}7ar6Ta-&jl{rdRNlSx2QflbZ+ph0Yr!Ck`yvxQA^gp$BMSz ztS9>txt!yIj|#<5p0&6N;4cl)?eC*`91qYu!$%=hQW#LGlNgSKeXg-nTrDxz&TnD2 zCha0bzyto?_-Tu=6NNm?6U~#V2d_d^5hF)I4>fofXo8$VIy7tF*d^p5>alb95VdHW zeenbXm9y_teonEvE#N?t#H45r^#LW404l5ZX|oV23p6p344#P4zvnADDXiI<{|FzD z-~muxydmm6cr!l}9>27dWMFidQa^Zx0#S))lVHlAk85_wU9{tt#EYJPG~Qe8)v0%# 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