---
title: Windows 节点的资源管理
content_type: concept
weight: 75
---
<!--
reviewers:
- jayunit100
- jsturtevant
- marosset
- perithompson
title: Resource Management for Windows nodes
content_type: concept
weight: 75
-->

<!-- overview 
This page outlines the differences in how resources are managed between Linux and Windows.
-->
本页概述了 Linux 和 Windows 在资源管理方式上的区别。

<!-- body -->
<!--
On Linux nodes, {{< glossary_tooltip text="cgroups" term_id="cgroup" >}} are used
as a pod boundary for resource control. Containers are created within that boundary for network, process and file system isolation. The Linux cgroup APIs can be used to gather CPU, I/O, and memory use statistics.

In contrast, Windows uses a [_job object_](https://docs.microsoft.com/windows/win32/procthread/job-objects) per container with a system namespace filter
to contain all processes in a container and provide logical isolation from the
host. (Job objects are a Windows process isolation mechanism and are different from what Kubernetes refers to as a {{< glossary_tooltip term_id="job" text="Job" >}}).

There is no way to run a Windows container without the namespace filtering in
place. This means that system privileges cannot be asserted in the context of the
host, and thus privileged containers are not available on Windows.
Containers cannot assume an identity from the host because the Security Account Manager (SAM) is separate.
-->
在 Linux 节点上，{{< glossary_tooltip text="cgroup" term_id="cgroup" >}} 用作资源控制的 Pod 边界。
在这个边界内创建容器以便于隔离网络、进程和文件系统。
Linux cgroup API 可用于收集 CPU、I/O 和内存使用统计数据。

与此相反，Windows 中每个容器对应一个[**作业对象**](https://docs.microsoft.com/zh-cn/windows/win32/procthread/job-objects)，
与系统命名空间过滤器一起使用，将所有进程包含在一个容器中，提供与主机的逻辑隔离。
（作业对象是一种 Windows 进程隔离机制，不同于 Kubernetes 提及的 {{< glossary_tooltip term_id="job" text="Job" >}})。

如果没有命名空间过滤，就无法运行 Windows 容器。
这意味着在主机环境中无法让系统特权生效，因此特权容器在 Windows 上不可用。
容器不能使用来自主机的标识，因为安全帐户管理器（Security Account Manager，SAM）是独立的。

<!--
## Memory management {#resource-management-memory}

Windows does not have an out-of-memory process killer as Linux does. Windows always
treats all user-mode memory allocations as virtual, and pagefiles are mandatory.

Windows nodes do not overcommit memory for processes. The
net effect is that Windows won't reach out of memory conditions the same way Linux
does, and processes page to disk instead of being subject to out of memory (OOM)
termination. If memory is over-provisioned and all physical memory is exhausted,
then paging can slow down performance.
-->
## 内存管理 {#resource-management-memory}

Windows 不像 Linux 一样提供杀手（killer）机制，杀死内存不足的进程。
Windows 始终将所有用户态内存分配视为虚拟内存，并强制使用页面文件（pagefile）。

Windows 节点不会为进程过量使用内存。
最终结果是 Windows 不会像 Linux 那样达到内存不足的情况，Windows 将进程页面放到磁盘，
不会因为内存不足（OOM）而终止进程。
如果内存配置过量且所有物理内存都已耗尽，则换页性能就会降低。
<!--
## CPU management {#resource-management-cpu}

Windows can limit the amount of CPU time allocated for different processes but cannot
guarantee a minimum amount of CPU time.

On Windows, the kubelet supports a command-line flag to set the
[scheduling priority](https://docs.microsoft.com/windows/win32/procthread/scheduling-priorities) of the
kubelet process: `--windows-priorityclass`. This flag allows the kubelet process to get
more CPU time slices when compared to other processes running on the Windows host.
More information on the allowable values and their meaning is available at
[Windows Priority Classes](https://docs.microsoft.com/en-us/windows/win32/procthread/scheduling-priorities#priority-class).
To ensure that running Pods do not starve the kubelet of CPU cycles, set this flag to `ABOVE_NORMAL_PRIORITY_CLASS` or above.
-->
## CPU 管理 {#resource-management-cpu}

Windows 可以限制为不同进程分配的 CPU 时间长度，但无法保证最小的 CPU 时间长度。

在 Windows 上，kubelet 支持使用命令行标志来设置 kubelet 进程的[调度优先级](https://docs.microsoft.com/zh-cn/windows/win32/procthread/scheduling-priorities)：
`--windows-priorityclass`。
与 Windows 主机上运行的其他进程相比，此标志允许 kubelet 进程获取更多的 CPU 时间片。
有关允许值及其含义的更多信息，请访问 [Windows 优先级类](https://docs.microsoft.com/zh-cn/windows/win32/procthread/scheduling-priorities#priority-class)。
为了确保运行的 Pod 不会耗尽 kubelet 的 CPU 时钟周期，
要将此标志设置为 `ABOVE_NORMAL_PRIORITY_CLASS` 或更高。

<!--
## Resource reservation {#resource-reservation}

To account for memory and CPU used by the operating system, the container runtime, and by
Kubernetes host processes such as the kubelet, you can (and should) reserve
memory and CPU resources with the  `--kube-reserved` and/or `--system-reserved` kubelet flags.
On Windows these values are only used to calculate the node's
[allocatable](/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable) resources.
-->
## 资源预留 {#resource-reservation}

为了满足操作系统、容器运行时和 kubelet 等 Kubernetes 主机进程使用的内存和 CPU，
你可以（且应该）用 `--kube-reserved` 和/或 `--system-reserved` kubelet 标志来预留内存和 CPU 资源。
在 Windows 上，这些值仅用于计算节点的[可分配](/zh-cn/docs/tasks/administer-cluster/reserve-compute-resources/#node-allocatable)资源。

<!--
As you deploy workloads, set resource memory and CPU limits on containers.
This also subtracts from `NodeAllocatable` and helps the cluster-wide scheduler in determining which pods to place on which nodes.

Scheduling pods without limits may over-provision the Windows nodes and in extreme
cases can cause the nodes to become unhealthy.
-->
{{< caution >}}
在你部署工作负载时，需对容器设置内存和 CPU 资源的限制。
这也会从 `NodeAllocatable` 中减去，帮助集群范围的调度器决定哪些 Pod 放到哪些节点上。

若调度 Pod 时未设置限制值，可能对 Windows 节点过量配置资源。
在极端情况下，这会让节点变得不健康。
{{< /caution >}}

<!--
On Windows, a good practice is to reserve at least 2GiB of memory.

To determine how much CPU to reserve,
identify the maximum pod density for each node and monitor the CPU usage of
the system services running there, then choose a value that meets your workload needs.
-->
在 Windows 上，一种好的做法是预留至少 2GiB 的内存。

要决定预留多少 CPU，需明确每个节点的最大 Pod 密度，
并监控节点上运行的系统服务的 CPU 使用率，然后选择一个满足工作负载需求的值。