From 5abd960d9c1c98c3ae9c844522b0ea181b2ceea9 Mon Sep 17 00:00:00 2001
From: Qiming Teng <tengqim@cn.ibm.com>
Date: Fri, 20 Nov 2020 11:42:09 +0800
Subject: [PATCH] [zh] Sync
 tasks/debug-application-cluster/resource-metrics-pipeline.md

---
 .../resource-metrics-pipeline.md              | 76 +++++++++++--------
 1 file changed, 44 insertions(+), 32 deletions(-)

diff --git a/content/zh/docs/tasks/debug-application-cluster/resource-metrics-pipeline.md b/content/zh/docs/tasks/debug-application-cluster/resource-metrics-pipeline.md
index fb878af5de..91cfe11c46 100644
--- a/content/zh/docs/tasks/debug-application-cluster/resource-metrics-pipeline.md
+++ b/content/zh/docs/tasks/debug-application-cluster/resource-metrics-pipeline.md
@@ -14,28 +14,29 @@ content_type: concept
 
 <!--
 Resource usage metrics, such as container CPU and memory usage,
-are available in Kubernetes through the Metrics API. These metrics can be either accessed directly
-by user, for example by using `kubectl top` command, or used by a controller in the cluster, e.g.
+are available in Kubernetes through the Metrics API. These metrics can be accessed either directly
+by the user with the `kubectl top` command, or by a controller in the cluster, for example
 Horizontal Pod Autoscaler, to make decisions.
 -->
 资源使用指标，例如容器 CPU 和内存使用率，可通过 Metrics API 在 Kubernetes 中获得。
-这些指标可以直接被用户访问，比如使用 `kubectl top` 命令行，或者这些指标由集群中的控制器使用，
-例如，Horizontal Pod Autoscaler，使用这些指标来做决策。
+这些指标可以直接被用户访问，比如使用 `kubectl top` 命令行，或者被集群中的控制器
+（例如 Horizontal Pod Autoscalers) 使用来做决策。
 
 <!-- body -->
 
 <!--
 ## The Metrics API
 
-Through the Metrics API you can get the amount of resource currently used
+Through the Metrics API, you can get the amount of resource currently used
 by a given node or a given pod. This API doesn't store the metric values,
-so it's not possible for example to get the amount of resources used by a
+so it's not possible, for example, to get the amount of resources used by a
 given node 10 minutes ago.
 -->
 ## Metrics API   {#the-metrics-api}
 
 通过 Metrics API，你可以获得指定节点或 Pod 当前使用的资源量。
-此 API 不存储指标值，因此想要获取某个指定节点 10 分钟前的资源使用量是不可能的。
+此 API 不存储指标值，因此想要获取某个指定节点 10 分钟前的
+资源使用量是不可能的。
 
 <!--
 The API is no different from any other API:
@@ -43,12 +44,12 @@ The API is no different from any other API:
 此 API 与其他 API 没有区别：
 
 <!--
-- it is discoverable through the same endpoint as the other Kubernetes APIs under `/apis/metrics.k8s.io/` path
-- it offers the same security, scalability and reliability guarantees
+- it is discoverable through the same endpoint as the other Kubernetes APIs under the path: `/apis/metrics.k8s.io/`
+- it offers the same security, scalability, and reliability guarantees
 -->
-- 此 API 和其它 Kubernetes API 一起位于同一端点（endpoint）之下，是可发现的，
+- 此 API 和其它 Kubernetes API 一起位于同一端点（endpoint）之下且可发现，
   路径为 `/apis/metrics.k8s.io/` 
-- 它提供相同的安全性、可扩展性和可靠性保证
+- 它具有相同的安全性、可扩展性和可靠性保证
 
 <!--
 The API is defined in [k8s.io/metrics](https://github.com/kubernetes/metrics/blob/master/pkg/apis/metrics/v1beta1/types.go)
@@ -69,7 +70,11 @@ Metrics API 需要在集群中部署 Metrics Server。否则它将不可用。
 
 ### CPU
 
-CPU is reported as the average usage, in [CPU cores](/docs/concepts/configuration/manage-compute-resources-container/#meaning-of-cpu), over a period of time. This value is derived by taking a rate over a cumulative CPU counter provided by the kernel (in both Linux and Windows kernels). The kubelet chooses the window  for the rate calculation.
+CPU is reported as the average usage, in
+[CPU cores](/docs/concepts/configuration/manage-resources-containers/#meaning-of-cpu),
+over a period of time. This value is derived by taking a rate over a cumulative CPU counter
+provided by the kernel (in both Linux and Windows kernels).
+The kubelet chooses the window for the rate calculation.
 -->
 ## 度量资源用量   {#measuring-resource-usage}
 
@@ -80,6 +85,15 @@ CPU 用量按其一段时间内的平均值统计，单位为
 此度量值通过在内核（包括 Linux 和 Windows）提供的累积 CPU 计数器乘以一个系数得到。
 `kubelet` 组件负责选择计算系数所使用的窗口大小。
 
+<!--
+### Memory
+
+Memory is reported as the working set, in bytes, at the instant the metric was collected.
+In an ideal world, the "working set" is the amount of memory in-use that cannot be freed under memory pressure.
+However, calculation of the working set varies by host OS, and generally makes heavy use of heuristics to produce an estimate.
+It includes all anonymous (non-file-backed) memory since Kubernetes does not support swap.
+The metric typically also includes some cached (file-backed) memory, because the host OS cannot always reclaim such pages.
+-->
 ### 内存  {#memory}
 
 内存用量按工作集（Working Set）的大小字节数统计，其数值为收集度量值的那一刻的内存用量。
@@ -94,37 +108,35 @@ CPU 用量按其一段时间内的平均值统计，单位为
 <!--
 ## Metrics Server
 
-[Metrics Server](https://github.com/kubernetes-incubator/metrics-server) is a cluster-wide aggregator of resource usage data.
-Starting from Kubernetes 1.8 it's deployed by default in clusters created by `kube-up.sh` script
+[Metrics Server](https://github.com/kubernetes-sigs/metrics-server) is a cluster-wide aggregator of resource usage data.
+By default, it is deployed in clusters created by `kube-up.sh` script
 as a Deployment object. If you use a different Kubernetes setup mechanism you can deploy it using the provided
-[deployment yamls](https://github.com/kubernetes-incubator/metrics-server/tree/master/deploy).
-It's supported in Kubernetes 1.7+ (see details below).
+[deployment components.yaml](https://github.com/kubernetes-sigs/metrics-server/releases) file.
 -->
 ## Metrics 服务器    {#metrics-server}
 
-[Metrics 服务器](https://github.com/kubernetes-incubator/metrics-server)是集群范围资源使用数据的聚合器。
-在由 `kube-up.sh` 脚本创建的集群中默认会以 Deployment 的形式被部署。
+[Metrics 服务器](https://github.com/kubernetes-sings/metrics-server)
+是集群范围资源用量数据的聚合器。
+默认情况下，在由 `kube-up.sh` 脚本创建的集群中会以 Deployment 的形式被部署。
 如果你使用其他 Kubernetes 安装方法，则可以使用提供的
-[deployment yamls](https://github.com/kubernetes-incubator/metrics-server/tree/master/deploy)
+[部署组件 components.yaml](https://github.com/kubernetes-incubator/metrics-server/tree/master/deploy)
 来部署。
 
 <!--
-Metric server collects metrics from the Summary API, exposed by [Kubelet](/docs/admin/kubelet/) on each node.
+Metric server collects metrics from the Summary API, exposed by
+[Kubelet](/docs/reference/command-line-tools-reference/kubelet/) on each node, and is registered with the main API server via
+[Kubernetes aggregator](/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/).
 -->
-Metric server 从每个节点上的 [Kubelet](/zh/docs/reference/command-line-tools-reference/kubelet/)
+Metric 服务器从每个节点上的 [kubelet](/zh/docs/reference/command-line-tools-reference/kubelet/)
 公开的 Summary API 中采集指标信息。
+该 API 通过
+[Kubernetes 聚合器](/zh/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/)
+注册到主 API 服务器上。
 
 <!--
-Metrics Server registered in the main API server through
-[Kubernetes aggregator](/docs/concepts/api-extension/apiserver-aggregation/),
-which was introduced in Kubernetes 1.7.
+Learn more about the metrics server in
+[the design doc](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md).
 -->
-Metrics 服务器通过
-[Kubernetes 聚合器](/zh/docs/concepts/extend-kubernetes/api-extension/apiserver-aggregation/) 
-注册到主 API 服务器。
-
-<!--
-Learn more about the metrics server in [the design doc](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md).
--->
-在[设计文档](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md)中可以了解到有关 Metrics 服务器的更多信息。
+在[设计文档](https://github.com/kubernetes/community/blob/master/contributors/design-proposals/instrumentation/metrics-server.md)
+中可以了解到有关 Metrics 服务器的更多信息。