website/content/zh-cn/docs/concepts/scheduling-eviction/pod-overhead.md

---
title: Pod 开销
content_type: concept
weight: 30
---

<!--
---
reviewers:
- dchen1107
- egernst
- tallclair
title: Pod Overhead
content_type: concept
weight: 30
---
-->

<!-- overview -->

{{< feature-state for_k8s_version="v1.24" state="stable" >}}

<!--
When you run a Pod on a Node, the Pod itself takes an amount of system resources. These
resources are additional to the resources needed to run the container(s) inside the Pod.
In Kubernetes, _Pod Overhead_ is a way to account for the resources consumed by the Pod
infrastructure on top of the container requests & limits.
-->

在节点上运行 Pod 时，Pod 本身占用大量系统资源。这些是运行 Pod 内容器所需资源之外的资源。
在 Kubernetes 中，_POD 开销_ 是一种方法，用于计算 Pod 基础设施在容器请求和限制之上消耗的资源。

<!-- body -->


<!--
In Kubernetes, the Pod's overhead is set at
[admission](/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks)
time according to the overhead associated with the Pod's
[RuntimeClass](/docs/concepts/containers/runtime-class/).
-->

在 Kubernetes 中，Pod 的开销是根据与 Pod 的 [RuntimeClass](/zh-cn/docs/concepts/containers/runtime-class/)
相关联的开销在[准入](/zh-cn/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks)时设置的。

<!--
When Pod Overhead is enabled, the overhead is considered in addition to the sum of container
resource requests when scheduling a Pod. Similarly,the kubelet will include the Pod overhead when sizing
the Pod cgroup, and when carrying out Pod eviction ranking.
-->

如果启用了 Pod Overhead，在调度 Pod 时，除了考虑容器资源请求的总和外，还要考虑 Pod 开销。
类似地，kubelet 将在确定 Pod cgroups 的大小和执行 Pod 驱逐排序时也会考虑 Pod 开销。

<!--
## Configuring Pod overhead {#set-up}
-->
## 配置 Pod 开销 {#set-up}

<!--
You need to make sure a `RuntimeClass` is utilized which defines the `overhead` field.
-->
你需要确保使用一个定义了 `overhead` 字段的 `RuntimeClass`。

<!--
## Usage example
-->
## 使用示例

<!--
To work with Pod overhead, you need a RuntimeClass that defines the `overhead` field. As
an example, you could use the following RuntimeClass definition with a virtualization container
runtime that uses around 120MiB per Pod for the virtual machine and the guest OS:
-->
要使用 Pod 开销，你需要一个定义了 `overhead` 字段的 RuntimeClass。
作为例子，下面的 RuntimeClass 定义中包含一个虚拟化所用的容器运行时，
RuntimeClass 如下，其中每个 Pod 大约使用 120MiB 用来运行虚拟机和寄宿操作系统：

```yaml
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: kata-fc
handler: kata-fc
overhead:
  podFixed:
    memory: "120Mi"
    cpu: "250m"
```

<!--
Workloads which are created which specify the `kata-fc` RuntimeClass handler will take the memory and
cpu overheads into account for resource quota calculations, node scheduling, as well as Pod cgroup sizing.

Consider running the given example workload, test-pod:
-->
通过指定 `kata-fc` RuntimeClass 处理程序创建的工作负载会将内存和 CPU
开销计入资源配额计算、节点调度以及 Pod cgroup 尺寸确定。

假设我们运行下面给出的工作负载示例 test-pod:

```yaml
apiVersion: v1
kind: Pod
metadata:
  name: test-pod
spec:
  runtimeClassName: kata-fc
  containers:
  - name: busybox-ctr
    image: busybox:1.28
    stdin: true
    tty: true
    resources:
      limits:
        cpu: 500m
        memory: 100Mi
  - name: nginx-ctr
    image: nginx
    resources:
      limits:
        cpu: 1500m
        memory: 100Mi
```

<!--
At admission time the RuntimeClass [admission controller](/docs/reference/access-authn-authz/admission-controllers/)
updates the workload's PodSpec to include the `overhead` as described in the RuntimeClass. If the PodSpec already has this field defined,
the Pod will be rejected. In the given example, since only the RuntimeClass name is specified, the admission controller mutates the Pod
to include an `overhead`.
-->
在准入阶段 RuntimeClass [准入控制器](/zh-cn/docs/reference/access-authn-authz/admission-controllers/)
更新工作负载的 PodSpec 以包含
RuntimeClass 中定义的 `overhead`。如果 PodSpec 中已定义该字段，该 Pod 将会被拒绝。
在这个例子中，由于只指定了 RuntimeClass 名称，所以准入控制器更新了 Pod，使之包含 `overhead`。

<!--
After the RuntimeClass admission controller, you can check the updated PodSpec:
-->
在 RuntimeClass 准入控制器进行修改后，你可以查看更新后的 PodSpec：
```bash
kubectl get pod test-pod -o jsonpath='{.spec.overhead}'
```

<!--
The output is:
-->
输出：
```
map[cpu:250m memory:120Mi]
```

<!--
If a [ResourceQuota](/docs/concepts/policy/resource-quotas/) is defined, the sum of container requests as well as the
`overhead` field are counted.
 -->
如果定义了 [ResourceQuata](/zh-cn/docs/concepts/policy/resource-quotas/), 
则容器请求的总量以及 `overhead` 字段都将计算在内。

<!--
When the kube-scheduler is deciding which node should run a new Pod, the scheduler considers that Pod's
`overhead` as well as the sum of container requests for that Pod. For this example, the scheduler adds the
requests and the overhead, then looks for a node that has 2.25 CPU and 320 MiB of memory available.
-->
当 kube-scheduler 决定在哪一个节点调度运行新的 Pod 时，调度器会兼顾该 Pod 的
`overhead` 以及该 Pod 的容器请求总量。在这个示例中，调度器将资源请求和开销相加，
然后寻找具备 2.25 CPU 和 320 MiB 内存可用的节点。

<!--
Once a Pod is scheduled to a node, the kubelet on that node creates a new {{< glossary_tooltip
text="cgroup" term_id="cgroup" >}} for the Pod. It is within this pod that the underlying
container runtime will create containers.
-->
一旦 Pod 被调度到了某个节点， 该节点上的 kubelet 将为该 Pod 新建一个 
{{< glossary_tooltip text="cgroup" term_id="cgroup" >}}。 底层容器运行时将在这个
Pod 中创建容器。

<!--
If the resource has a limit defined for each container (Guaranteed QoS or Burstable QoS with limits defined),
the kubelet will set an upper limit for the pod cgroup associated with that resource (cpu.cfs_quota_us for CPU
and memory.limit_in_bytes memory). This upper limit is based on the sum of the container limits plus the `overhead`
defined in the PodSpec.
-->
如果该资源对每一个容器都定义了一个限制（定义了限制值的 Guaranteed QoS 或者
Burstable QoS），kubelet 会为与该资源（CPU 的 `cpu.cfs_quota_us` 以及内存的
`memory.limit_in_bytes`）
相关的 Pod cgroup 设定一个上限。该上限基于 PodSpec 中定义的容器限制总量与 `overhead` 之和。

<!--
For CPU, if the Pod is Guaranteed or Burstable QoS, the kubelet will set `cpu.shares` based on the
sum of container requests plus the `overhead` defined in the PodSpec.
-->
对于 CPU，如果 Pod 的 QoS 是 Guaranteed 或者 Burstable，kubelet 会基于容器请求总量与
PodSpec 中定义的 `overhead` 之和设置 `cpu.shares`。

<!--
Looking at our example, verify the container requests for the workload:
-->
请看这个例子，验证工作负载的容器请求：

```bash
kubectl get pod test-pod -o jsonpath='{.spec.containers[*].resources.limits}'
```

<!--
The total container requests are 2000m CPU and 200MiB of memory:
-->
容器请求总计 2000m CPU 和 200MiB 内存：

```
map[cpu: 500m memory:100Mi] map[cpu:1500m memory:100Mi]
```

<!--
Check this against what is observed by the node:
 -->
对照从节点观察到的情况来检查一下：

```bash
kubectl describe node | grep test-pod -B2
```

<!--
The output shows requests for 2250m CPU, and for 320MiB of memory. The requests include Pod overhead:
-->
该输出显示请求了 2250m CPU 以及 320MiB 内存。请求包含了 Pod 开销在内：
```
  Namespace    Name       CPU Requests  CPU Limits   Memory Requests  Memory Limits  AGE
  ---------    ----       ------------  ----------   ---------------  -------------  ---
  default      test-pod   2250m (56%)   2250m (56%)  320Mi (1%)       320Mi (1%)     36m
```

<!--
## Verify Pod cgroup limits
-->
## 验证 Pod cgroup 限制

<!--
Check the Pod's memory cgroups on the node where the workload is running. In the following example,
[`crictl`](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md)
is used on the node, which provides a CLI for CRI-compatible container runtimes. This is an
advanced example to show Pod overhead behavior, and it is not expected that users should need to check
cgroups directly on the node.

First, on the particular node, determine the Pod identifier:
-->
在工作负载所运行的节点上检查 Pod 的内存 cgroups。在接下来的例子中，
将在该节点上使用具备 CRI 兼容的容器运行时命令行工具
[`crictl`](https://github.com/kubernetes-sigs/cri-tools/blob/master/docs/crictl.md)。
这是一个显示 Pod 开销行为的高级示例， 预计用户不需要直接在节点上检查 cgroups。
首先在特定的节点上确定该 Pod 的标识符：

<!--
```bash
# Run this on the node where the Pod is scheduled
-->
```bash
# 在该 Pod 被调度到的节点上执行如下命令：
POD_ID="$(sudo crictl pods --name test-pod -q)"
```

<!--
From this, you can determine the cgroup path for the Pod:
 -->
可以依此判断该 Pod 的 cgroup 路径：

<!--
```bash
# Run this on the node where the Pod is scheduled
-->
```bash
# 在该 Pod 被调度到的节点上执行如下命令：
sudo crictl inspectp -o=json $POD_ID | grep cgroupsPath
```

<!--
The resulting cgroup path includes the Pod's `pause` container. The Pod level cgroup is one directory above.
-->
执行结果的 cgroup 路径中包含了该 Pod 的 `pause` 容器。Pod 级别的 cgroup 在即上一层目录。

```
  "cgroupsPath": "/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/7ccf55aee35dd16aca4189c952d83487297f3cd760f1bbf09620e206e7d0c27a"
```

<!--
In this specific case, the pod cgroup path is `kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2`.
Verify the Pod level cgroup setting for memory:
-->
在这个例子中，该 Pod 的 cgroup 路径是 `kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2`。
验证内存的 Pod 级别 cgroup 设置：

<!--
```bash
# Run this on the node where the Pod is scheduled.
# Also, change the name of the cgroup to match the cgroup allocated for your pod.
-->
```bash
# 在该 Pod 被调度到的节点上执行这个命令。
# 另外，修改 cgroup 的名称以匹配为该 Pod 分配的 cgroup。
 cat /sys/fs/cgroup/memory/kubepods/podd7f4b509-cf94-4951-9417-d1087c92a5b2/memory.limit_in_bytes
```

<!--
This is 320 MiB, as expected:
-->
和预期的一样，这一数值为 320 MiB。

```
335544320
```

<!--
### Observability
-->
### 可观察性

<!--
Some `kube_pod_overhead_*` metrics are available in [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics)
to help identify when Pod overhead is being utilized and to help observe stability of workloads
running with a defined overhead.
-->
在 [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) 中可以通过
`kube_pod_overhead_*` 指标来协助确定何时使用 Pod 开销，
以及协助观察以一个既定开销运行的工作负载的稳定性。
该特性在 kube-state-metrics 的 1.9 发行版本中不可用，不过预计将在后续版本中发布。
在此之前，用户需要从源代码构建 kube-state-metrics。

## {{% heading "whatsnext" %}}

<!--
* Learn more about [RuntimeClass](/docs/concepts/containers/runtime-class/)
* Read the [PodOverhead Design](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/688-pod-overhead)
  enhancement proposal for extra context
-->
* 学习更多关于 [RuntimeClass](/zh-cn/docs/concepts/containers/runtime-class/) 的信息
* 阅读 [PodOverhead 设计](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/688-pod-overhead)增强建议以获取更多上下文