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# KEP-2000: Graceful Node Shutdown

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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](#story-1)
    - [Story 2](#story-2)
  - [Background on Linux Shutdown](#background-on-linux-shutdown)
  - [Background on Inhibitors](#background-on-inhibitors)
  - [Implementation](#implementation)
  - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
  - [Test Plan](#test-plan)
  - [Graduation Criteria](#graduation-criteria)
    - [Alpha Graduation](#alpha-graduation)
    - [Alpha -&gt; Beta Graduation](#alpha---beta-graduation)
    - [Beta -&gt; GA Graduation](#beta---ga-graduation)
  - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
  - [Version Skew Strategy](#version-skew-strategy)
- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire)
  - [Feature Enablement and Rollback](#feature-enablement-and-rollback)
  - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning)
  - [Monitoring Requirements](#monitoring-requirements)
  - [Dependencies](#dependencies)
  - [Scalability](#scalability)
  - [Troubleshooting](#troubleshooting)
- [Implementation History](#implementation-history)
- [Drawbacks](#drawbacks)
- [Alternatives](#alternatives)
- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
<!-- /toc -->

## Release Signoff Checklist

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

## Summary

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Kubelet should be aware of node shutdown and trigger graceful shutdown of pods
during a machine shutdown.

## Motivation

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Users and cluster administrators expect that pods will adhere to expected pod
lifecycle including pod termination. Currently, when a node shuts down, pods do
not follow the expected pod termination lifecycle and are not terminated
gracefully which can cause issues for some workloads. This KEP aims to address
this problem by making the kubelet aware of the underlying node shutdown.
Kubelet will propagate this signal to pods ensuring they can shutdown as
gracefully as possible.

### Goals

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*   Make kubelet aware of underlying node shutdown event and trigger pod
    termination with sufficient grace period to shutdown properly
*   Handle node shutdown in cloud-provider agnostic way
*   Introduce minimal shutdown delay in order to shutdown node soon as possible
    (but not sooner)
*   Focus on handling shutdown on systemd based machines

### Non-Goals

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*   Let users modify or change existing pod lifecycle or introduce new inner
    pod depencides / shutdown ordering
*   Support every linux init and ACPI event handling mechanism (focus on widely
    used logind from systemd)
*   Provide guarantee to handle all cases of graceful node shutdown, for
    example abrupt shutdown or sudden power cable pull can’t result in graceful
    shutdown

## Proposal

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### User Stories (Optional)

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#### Story 1

*   As a cluster administrator, I can configure the nodes in my cluster to
    allocate X seconds for my pods to terminate gracefully during a node
    shutdown

#### Story 2

*   As a developer I can expect that my pods will terminate gracefully during
    node shutdowns

### Background on Linux Shutdown

In the context of this KEP, shutdown is referred to as shutdown of the
underlying machine. On most linux distros shutdown can be initiated via a
variety of methods for example:

1. `shutdown -h now`
2. `shutdown -h +30` `#schedule a delayed shutdown in 30mins`
3. `systemctl poweroff`
4. Physically pressing the power button on the machine
5. If a machine is a VM, the underlying hypervisor can press the “virtual”
   power button
6. For a cloud instance, stopping the instance via Cloud API, e.g. via `gcloud
   compute instances stop`. Depending on the cloud provider, this may result in
   virtual power button press by the underlying hypervisor.

Note: The use of `shutdown -h now` is dependent on systemd version. This is explored in Github issue [#124039](https://github.com/kubernetes/kubernetes/issues/124039)

Some of these cases will involve the machine receiving an ACPI event to change
the power state. The machine can go from G0 (working state) to G2 (Soft Off)
and finally to G3 (Off) [more info on ACPI
states](https://en.wikipedia.org/wiki/Advanced_Configuration_and_Power_Interface).
On Linux, prior to shutdown usually a system daemon will listen to these events
and perform some series of actions prior to userspace calling the
[reboot(2)](https://man7.org/linux/man-pages/man2/reboot.2.html) systemcall
with `LINUX_REBOOT_CMD_POWER_OFF` or `LINUX_REBOOT_CMD_HALT` to actually
shutdown the machine.

Historically, ACPI events were often handled by the
[acpid](https://wiki.archlinux.org/index.php/Acpid) daemon which uses a variety
of mechanisms to watch ACPI events (i.e. reading `/proc/acpi/event` or
`/dev/input/eventX` to react to power button presses). However, in most modern
linux distros today,
[systemd-logind](https://www.freedesktop.org/wiki/Software/systemd/logind/)
has taken over as the main component [reacting to ACPI
events](https://wiki.archlinux.org/index.php/Power_management#ACPI_events) and
initiating shutdown of the machine. On a system with
systemd-logind, for example, a trigger of the power button will
result in the systemd target
[poweroff](https://www.freedesktop.org/software/systemd/man/systemd-halt.service.html)
being run (see
[HandlePowerKey](https://www.freedesktop.org/software/systemd/man/logind.conf.html),
which will terminate all the systemd services running on the machine and
eventually shut it down. However, in the context of kubernetes, systemd is not
aware of the pods and containers running on the machine and systemd will simply
kill them as regular linux processes.

### Background on Inhibitors

`systemd-logind` provides the ability for applications to delay shutdown and
perform some series of actions before the shutdown completes through a
mechanism called ["Inhibitor
Locks".](https://www.freedesktop.org/wiki/Software/systemd/inhibit/)
Applications can request to delay shutdown by taking an inhibitor lock by
sending messages to logind over dbus. Applications can request up to
`InhibitDelayMaxSec` (a setting configured in `logind.conf`) for delay based
locks, which allow applications to receive sleep and shutdown events, and block
the shutdown from proceeding by `InhibitDelayMaxSec` period to execute some
critical work prior to shutdown/sleep. Inhibitor Locks were introduced to
[systemd 183](https://lwn.net/Articles/499480/) (released in 2012).

We believe that making use of systemd is a reasonable approach considering
almost all new popular linux distros are systemd based (RHEL, Google COS,
Ubuntu, CentOS, Debian, Fedora, Flatcar Linux, see widespread
[adoption](https://en.wikipedia.org/wiki/Systemd#Adoption)) and [systemd
183](https://lwn.net/Articles/499480/) (released in 2012) features support for
inhibitors.

Thanks to @giuseppe for helping with getting systemd inhibitors working!

### Implementation

Introduce a new Kubelet Config setting, `kubeletConfig.ShutdownGracePeriod`,
defaulting to 0 seconds. Upon kubelet startup,

*   if the setting is greater than 0 seconds
    *   kubelet will check with dbus current `InhibitDelayMaxSec` to check if
        `kubeletConfig.ShutdownGracePeriod <=` `InhibitDelayMaxSec`.
*   if `kubeletConfig.ShutdownGracePeriod` > `InhibitDelayMaxSec`
    *   Kubelet will attempt to update the InhibitDelayMaxSec setting, by
        writing a config file to `/etc/systemd/logind.conf.d/kubelet.conf`, and
        sending a SIGHUP to logind to update the config setting to ensure that
        the ShutdownGracePeriod from kubelet config is equal to
        `InhibitDelayMaxSec`.

After updating the `InhibitDelayMaxSec` on the node if needed, Kubelet will
query the dbus for the final value of `InhibitDelayMaxSec` set on the node and
treat min(`InhibitDelayMaxSec`, `kubeletConfig.ShutdownGracePeriod`) as the
allocatable  shutdown grace period, which will be referred to in this KEP as
`ShutdownGracePeriod`.

Kubelet will register with dbus as a delay systemd inhibitor lock for the
`ShutdownGracePeriod` for the shutdown event. Kubelet will also register a
`PrepareForShutdown` signal which will be emitted prior to the shutdown. Upon
receiving the signal, Kubelet will have additional `ShutdownGracePeriod` time
before the actual node will initiate the shutdown.

**Handling the shutdown**

Upon a shutdown occurring, Kubelet will gracefully terminate all the pods
running on the node and update the Ready condition of the node to false with a
message `Node Shutting Down`, thereby ensuring new workloads will not get
scheduled to the node.

Since some of the pods running on the node are often critical for the the
workloads running on a node (e.g. logging pod daemonset, kubeproxy, kubedns)
etc, we choose to split the pods running on the node into two categories,
“critical system pods”, and regular pods. Critical system pods should be
terminated last, because for example, if the logging pod is terminated first,
logs from the other workloads will not be captured. Critical system pods are
identified as those that are in the `system-cluster-critical` or
`system-node-critical` [priority
classes](https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/#marking-pod-as-critical).

Upon shutdown Kubelet will:

1. Update the Node's `Ready` condition to `false`, with the reason `Node is
   shutting down`
2. Gracefully terminate all non critical system pods with a gracePeriodOverride
   computed as `min(podSpec.terminationGracePeriodSeconds,
   ShutdownGracePeriod-ShutdownGracePeriodCriticalPods)`
3. Gracefully terminate all critical system pods with gracePeriodOverride of
   `ShutdownGracePeriodCriticalPods` seconds

Kubelet will use the same existing
[killPod](https://github.com/kubernetes/kubernetes/blob/release-1.19/pkg/kubelet/pod_workers.go#L292)
function to perform the termination of pods, using `gracePeriodOverride` to set
the appropriate grace period. During the termination process, normal [pod
termination](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#pod-termination)
processes will apply, e.g. preStop Hooks will be called, `SIGTERM` to containers
delivered, etc.

To ensure `gracePeriodOverride` is respected, Github issue
[#92432](https://github.com/kubernetes/kubernetes/issues/92432) should also be
addressed to ensure that `gracePeriod` override will be respected for `preStop`
hooks.

POC: I’ve prototyped an initial POC
[here](https://github.com/bobbypage/kubernetes/tree/shutdown) of the proposed
implementation on the `shutdown` branch.


### Notes/Constraints/Caveats (Optional)

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### Risks and Mitigations

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*   Kubelet does not receive shutdown event or is able to create inhibitor lock
    *   Mitigation: Kubelet does not provide graceful shutdown to pods (same as
        today’s existing behavior). For alpha stage, to track shutdown behavior
        and if it was successful, we plan to add a debugging log statement just
        prior to kubelet's shutdown process being completed, so it's possible
        to verify if kubelet shutdown the node gracefully.
*   Kubelet is unable to update `InhibitDelayMaxSec` in logind to match that of
    `kubeletConfig.ShutdownGracePeriod`
    *   If there are multiple logind configuration file overrides in
        `/etc/systemd/logind.conf.d/`, logind will use the config file with the
        lexicographically latest name. As a result in rare cases, the kubelet’s
        InhibitDelayMaxSec conf file override may be overwritten by another
        config file (possibly placed by another service on the machine).
    *   Mitigation: Kubelet will use current value of `InhibitDelayMaxSec` from
        logind as the shutdown period which may be less than
        `kubeletConfig.ShutdownGracePeriod`.
*   OS / Distro does not use systemd or systemd version < 183
    *   Mitigation: Kubelet will not provide graceful shutdown to pods (same as
        today’s existing behavior).

## Design Details

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The design proposes adding a new KubeletConfig field `ShutdownGracePeriod` used
to specify total time period kubelet should delay shutdown by and thus total time
allocated to the graceful termination process.

In addition to `ShutdownGracePeriod`, another KubeletConfig field will be added
`ShutdownGracePeriodCriticalPods`. During the shutdown, the
`ShutdownGracePeriod-ShutdownGracePeriodCriticalPods` duration will be grace
period for non critical system pods like user workloads, while the remaining
time of `ShutdownGracePeriodCriticalPods` will be the grace period for critical
pods like node logging daemonsets.

```
type KubeletConfiguration struct {
    ...
    ShutdownGracePeriod metav1.Duration
    ShutdownGracePeriodCriticalPods metav1.Duration
}
```

Communication with systemd over dbus for (creating inhibitor lock, receiving
`PrepareForShutdown` callback, etc), will make use of the
`github.com/godbus/dbus/v5` package which is already included in
[`vendor/`](https://github.com/kubernetes/kubernetes/tree/release-1.19/vendor/github.com/godbus/dbus/v5).

Termination of pods will make use of the existing
[killPod](https://github.com/kubernetes/kubernetes/blob/release-1.19/pkg/kubelet/pod_workers.go#L292) function
from the `kubelet` package and specify the appropriate `gracePeriodOverride` as
necessary.

### Test Plan

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*   Unit tests for kubelet of handling shutdown event
*   New E2E tests to validate node graceful shutdown (note limitation that K8S
    E2E tests currently only run on GCE).
    *   Shutdown grace period unspecified, feature is not active
    *   Pod’s ExecStop and SIGTERM handlers are given gracePeriodSeconds for
        case when gracePeriodSeconds <= kubeletConfig.ShutdownGracePeriod
    *   Pod’s ExecStop and SIGTERM handlers are given
        kubeletConfig.ShutdownGracePeriod for case when gracePeriodSeconds >
        kubeletConfig.ShutdownGracePeriod

### Graduation Criteria

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#### Removing a Deprecated Flag

- Announce deprecation and support policy of the existing flag
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- Address feedback on usage/changed behavior, provided on GitHub issues
- Deprecate the flag

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

#### Alpha Graduation

* Implemented the feature for Linux (systemd) only
* Unit tests
  * Unit tests will mock out system components (i.e. systemd, inhibitors) for
    alpha
* Investigate how e2e tests can be implemented (e.g. may need to create fake
  shutdown event)

#### Alpha -> Beta Graduation

* Addresses feedback from alpha testers
* Sufficient E2E and unit testing

#### Beta -> GA Graduation

* Addresses feedback from beta
* Sufficient number of users using the feature
* Confident that no further API / kubelet config configuration options changes are needed
* Close on any remaining open issues & bugs

### Upgrade / Downgrade Strategy

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- 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?
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n/a

### Version Skew Strategy

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  in the kubelet? How does an n-2 kubelet without this feature available behave
  when this feature is used?
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  CRI or CNI may require updating that component before the kubelet.
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n/a

## Production Readiness Review Questionnaire

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### 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?**
  - [X] Feature gate (also fill in values in `kep.yaml`)
    - Feature gate name: `GracefulNodeShutdown`
    - Components depending on the feature gate:
      - `kubelet`
  - [ ] Other
    - Describe the mechanism:
    - Will enabling / disabling the feature require downtime of the control
      plane?
      - no
    - Will enabling / disabling the feature require downtime or reprovisioning
      of a node? (Do not assume `Dynamic Kubelet Config` feature is enabled).
      - yes (will require restart of kubelet)

* **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.

    * The main behavior change is that during a node shutdown, pods running on
      the node will be terminated gracefully.

* **Can the feature be disabled once it has been enabled (i.e. can we roll back
  the enablement)?**
  Also set `disable-supported` to `true` or `false` in `kep.yaml`.
  Describe the consequences on existing workloads (e.g., if this is a runtime
  feature, can it break the existing applications?).

    * Yes, the feature can be disabled by either disabling the feature gate, or
      setting `kubeletConfig.ShutdownGracePeriod` to 0 seconds.

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

    * Kubelet will attempt to perform graceful termination of pods during a
        node shutdown.

* **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.

    *   n/a

### Rollout, Upgrade and Rollback Planning

_This section must be completed when targeting beta graduation to a release._

* **How can a rollout fail? Can it impact already running workloads?**
  Try to be as paranoid as possible - e.g., what if some components will restart
   mid-rollout?

This feature should not impact rollouts.

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

N/A.

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

The feature is part of kubelet config so updating kubelet config should
enable/disable the feature; upgrade/downgrade is N/A.

* **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.

No.

### Monitoring Requirements

_This section must be completed when targeting beta graduation to a release._

* **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.

Check if the feature gate and kubelet config settings are enabled on a node.

* **What are the SLIs (Service Level Indicators) an operator can use to determine
the health of the service?**
  - [ ] Metrics
    - Metric name:
    - [Optional] Aggregation method:
    - Components exposing the metric:
  - [ ] Other (treat as last resort)
    - Details:

N/A

* **What are the reasonable SLOs (Service Level Objectives) for the above SLIs?**
  At a high level, this usually will be in the form of "high percentile of SLI
  per day <= X". 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

N/A.

* **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.).

N/A.

### Dependencies

_This section must be completed when targeting beta graduation 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:

No, this feature doesn't depend on any specific services running the cluster.
It only depends on systemd running on the node itself.

### Scalability

_For alpha, this section is encouraged: reviewers should consider these questions
and attempt to answer them._

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

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

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

No.

* **Will enabling / using this feature result in introducing new API types?**
  Describe them, providing:
  - API type
  - Supported number of objects per cluster
  - Supported number of objects per namespace (for namespace-scoped objects)

No.

* **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?**
  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)

No.

* **Will enabling / using this feature result in increasing time taken by any
operations covered by [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.

No.

* **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].

No.

### Troubleshooting

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.

_This section must be completed when targeting beta graduation to a release._

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

The feature does not depend on the API server / etcd.

* **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?**

[supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md
[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos

N/A.

## 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
-->

*   2020-05-26 - [Original GH issue #91472 filed](https://github.com/kubernetes/kubernetes/issues/91472)
*   2020-10-02 - [Initial KEP approved](https://github.com/kubernetes/enhancements/pull/2001)
*   2020-11-12 - [Initial Alpha implementation merged for k8s 1.20](https://github.com/kubernetes/kubernetes/pull/96129)
*   2020-11-20 - [Docs merged](https://github.com/kubernetes/website/pull/24918)

## 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.
-->

* Use systemd cgroup driver to set `TimeoutStopSec=` on scopes underlying
  containers
    * Set `TimeStopSec=` for the container scopes using the value set in the pod
      for termination grace period. The problem with this approach is that
      systemd doesn’t understand the prestop hooks. 
* Use systemd cgroup driver to set `Before=kubelet.service` on scopes
  underlying containers
    * Set `Before=kubelet.service` and container runtime service for the
      container scopes. Systemd would then stop the containers after the
      kubelet giving the kubelet a chance to stop the containers itself. This
      depends upon using the systemd cgroups driver and is coupled to systemd.
* Use systemd cgroup driver to set controller property on scope to delegate
  control to kubelet
    * Set Controller dbus property for the container scopes and set
      `After=kubelet.service` for the containers. Systemd would then signal the
      kubelet over dbus to delegate the container scope termination. This
      requires more work in the kubelet and is also coupled to systemd and the
      systemd cgroup driver.
* Don’t handle node shutdown events at all, and have users drain nodes before
  shutting them down.
    * This is not always possible, for example if the shutdown is controlled by
      some external system (e.g. Preemptible VMs).
* Avoid relying on systemd and logind and directly hook into ACPI events on the
  node.
    * Unfortunately, this can create conflicts because only one systemd daemon
      should be monitoring ACPI events. Additionally, if the system is using
      systemd but kubelet did not integrate with it, systemd by default would
      terminate kubelet and other processes during a shutdown event.
* Provide more configuration options on how to split time during shutdown (e.g.
  split between critical pods and user workloads). Need more feedback from the
  community here.

## 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.  -->
