244 lines
10 KiB
Markdown
244 lines
10 KiB
Markdown
---
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title: 为集群超配节点容量
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content_type: task
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weight: 10
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---
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<!--
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title: Overprovision Node Capacity For A Cluster
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content_type: task
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weight: 10
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-->
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<!-- overview -->
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<!--
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This page guides you through configuring {{< glossary_tooltip text="Node" term_id="node" >}}
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overprovisioning in your Kubernetes cluster. Node overprovisioning is a strategy that proactively
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reserves a portion of your cluster's compute resources. This reservation helps reduce the time
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required to schedule new pods during scaling events, enhancing your cluster's responsiveness
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to sudden spikes in traffic or workload demands.
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By maintaining some unused capacity, you ensure that resources are immediately available when
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new pods are created, preventing them from entering a pending state while the cluster scales up.
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-->
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本页指导你在 Kubernetes 集群中配置{{< glossary_tooltip text="节点" term_id="node" >}}超配。
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节点超配是一种主动预留部分集群计算资源的策略。这种预留有助于减少在扩缩容事件期间调度新 Pod 所需的时间，
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从而增强集群对突发流量或突发工作负载需求的响应能力。
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通过保持一些未使用的容量，确保在新 Pod 被创建时资源可以立即可用，防止 Pod 在集群扩缩容时进入 Pending 状态。
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## {{% heading "prerequisites" %}}
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<!--
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- You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with
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your cluster.
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- You should already have a basic understanding of
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[Deployments](/docs/concepts/workloads/controllers/deployment/),
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Pod {{< glossary_tooltip text="priority" term_id="pod-priority" >}},
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and {{< glossary_tooltip text="PriorityClasses" term_id="priority-class" >}}.
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- Your cluster must be set up with an [autoscaler](/docs/concepts/cluster-administration/cluster-autoscaling/)
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that manages nodes based on demand.
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-->
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- 你需要有一个 Kubernetes 集群，并且 kubectl 命令行工具必须被配置为与你的集群通信。
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- 你应该已经基本了解了 [Deployment](/zh-cn/docs/concepts/workloads/controllers/deployment/)、Pod
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{{<glossary_tooltip text="优先级" term_id="pod-priority">}}和
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{{< glossary_tooltip text="PriorityClass" term_id="priority-class" >}}。
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- 你的集群必须设置一个基于需求管理节点的[自动扩缩程序](/zh-cn/docs/concepts/cluster-administration/cluster-autoscaling/)。
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<!-- steps -->
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<!--
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## Create a PriorityClass
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Begin by defining a PriorityClass for the placeholder Pods. First, create a PriorityClass with a
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negative priority value, that you will shortly assign to the placeholder pods.
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Later, you will set up a Deployment that uses this PriorityClass
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-->
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## 创建 PriorityClass {#create-a-priorityclass}
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首先为占位 Pod 定义一个 PriorityClass。
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先创建一个优先级值为负数的 PriorityClass，稍后将其分配给占位 Pod。
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接下来，你将部署使用此 PriorityClass 的 Deployment。
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{{% code_sample language="yaml" file="priorityclass/low-priority-class.yaml" %}}
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<!--
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Then create the PriorityClass:
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-->
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然后创建 PriorityClass：
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```shell
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kubectl apply -f https://k8s.io/examples/priorityclass/low-priority-class.yaml
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```
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<!--
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You will next define a Deployment that uses the negative-priority PriorityClass and runs a minimal container.
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When you add this to your cluster, Kubernetes runs those placeholder pods to reserve capacity. Any time there
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is a capacity shortage, the control plane will pick one these placeholder pods as the first candidate to
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{{< glossary_tooltip text="preempt" term_id="preemption" >}}.
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-->
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接下来，你将定义一个 Deployment，使用优先级值为负数的 PriorityClass 并运行最小的容器。
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当你将此 Deployment 添加到集群中时，Kubernetes 会运行这些占位 Pod 以预留容量。
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每当出现容量短缺时，控制面将选择这些占位 Pod
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中的一个作为第一个候选者进行{{< glossary_tooltip text="抢占" term_id="preemption" >}}。
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<!--
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## Run Pods that request node capacity
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Review the sample manifest:
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-->
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## 运行请求节点容量的 Pod {#run-pods-that-request-node-capacity}
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查看样例清单：
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{{% code_sample language="yaml" file="deployments/deployment-with-capacity-reservation.yaml" %}}
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<!--
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### Pick a namespace for the placeholder pods
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You should select, or create, a {{< glossary_tooltip term_id="namespace" text="namespace">}}
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that the placeholder Pods will go into.
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-->
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### 为占位 Pod 挑选一个命名空间 {#pick-a-namespace-for-the-placeholder-pods}
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你应选择或创建占位 Pod 要进入的{{< glossary_tooltip term_id="namespace" text="命名空间">}}。
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<!--
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### Create the placeholder deployment
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Create a Deployment based on that manifest:
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```shell
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# Change the namespace name "example"
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kubectl --namespace example apply -f https://k8s.io/examples/deployments/deployment-with-capacity-reservation.yaml
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```
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-->
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### 创建占位 Deployment {#create-the-placeholder-deployment}
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基于该清单创建 Deployment：
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```shell
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# 你要更改命名空间名称 "example"
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kubectl --namespace example apply -f https://k8s.io/examples/deployments/deployment-with-capacity-reservation.yaml
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```
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<!--
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## Adjust placeholder resource requests
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Configure the resource requests and limits for the placeholder pods to define the amount of overprovisioned resources you want to maintain. This reservation ensures that a specific amount of CPU and memory is kept available for new pods.
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-->
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## 调整占位资源请求 {#adjust-placeholder-resource-requests}
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为占位 Pod 配置资源请求和限制，以定义你希望保持的超配资源量。
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这种预留确保为新 Pod 保留可以使用的、特定量的 CPU 和内存。
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<!--
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To edit the Deployment, modify the `resources` section in the Deployment manifest file
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to set appropriate requests and limits. You can download that file locally and then edit it
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with whichever text editor you prefer.
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You can also edit the Deployment using kubectl:
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-->
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要编辑 Deployment，可以修改 Deployment 清单文件中的 `resources` 一节，
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设置合适的 `requests` 和 `limits`。
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你可以将该文件下载到本地，然后用自己喜欢的文本编辑器进行编辑。
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你也可以使用 kubectl 来编辑 Deployment：
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```shell
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kubectl edit deployment capacity-reservation
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```
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<!--
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For example, to reserve 500m CPU and 1Gi memory across 5 placeholder pods,
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define the resource requests and limits for a single placeholder pod as follows:
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-->
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例如，要为 5 个占位 Pod 预留 500m CPU 和 1Gi 内存，请为单个占位 Pod 定义以下资源请求和限制：
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```yaml
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resources:
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requests:
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cpu: "100m"
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memory: "200Mi"
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limits:
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cpu: "100m"
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```
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<!--
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## Set the desired replica count
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### Calculate the total reserved resources
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-->
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## 设置所需的副本数量 {#set-the-desired-replica-count}
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### 计算总预留资源 {#calculate-the-total-reserved-resources}
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<!-- trailing whitespace in next paragraph is significant -->
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<!--
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For example, with 5 replicas each reserving 0.1 CPU and 200MiB of memory:
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Total CPU reserved: 5 × 0.1 = 0.5 (in the Pod specification, you'll write the quantity `500m`)
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Total memory reserved: 5 × 200MiB = 1GiB (in the Pod specification, you'll write `1 Gi`)
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To scale the Deployment, adjust the number of replicas based on your cluster's size and expected workload:
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-->
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例如，有 5 个副本，每个预留 0.1 CPU 和 200MiB 内存：
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CPU 预留总量：5 × 0.1 = 0.5（在 Pod 规约中，你将写入数量 `500m`）
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内存预留总量：5 × 200MiB = 1GiB（在 Pod 规约中，你将写入 `1 Gi`）
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要扩缩容 Deployment，请基于集群的大小和预期的工作负载调整副本数：
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```shell
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kubectl scale deployment capacity-reservation --replicas=5
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```
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<!--
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Verify the scaling:
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-->
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验证扩缩容效果：
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```shell
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kubectl get deployment capacity-reservation
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```
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<!--
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The output should reflect the updated number of replicas:
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-->
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输出应反映出更新后的副本数：
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```none
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NAME READY UP-TO-DATE AVAILABLE AGE
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capacity-reservation 5/5 5 5 2m
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```
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{{< note >}}
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<!--
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Some autoscalers, notably [Karpenter](/docs/concepts/cluster-administration/cluster-autoscaling/#autoscaler-karpenter),
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treat preferred affinity rules as hard rules when considering node scaling.
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If you use Karpenter or another node autoscaler that uses the same heuristic,
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the replica count you set here also sets a minimum node count for your cluster.
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-->
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一些自动扩缩组件，特别是
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[Karpenter](/zh-cn/docs/concepts/cluster-administration/cluster-autoscaling/#autoscaler-karpenter)，
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在考虑节点扩缩容时将偏好的亲和性规则视为硬性规则。如果你使用 Karpenter
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或其他使用同样启发式的节点扩缩容组件，你在此处设置的副本数也就是你的集群的最少节点数。
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{{< /note >}}
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## {{% heading "whatsnext" %}}
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<!--
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- Learn more about [PriorityClasses](/docs/concepts/scheduling-eviction/pod-priority-preemption/#priorityclass) and how they affect pod scheduling.
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- Explore [node autoscaling](/docs/concepts/cluster-administration/cluster-autoscaling/) to dynamically adjust your cluster's size based on workload demands.
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- Understand [Pod preemption](/docs/concepts/scheduling-eviction/pod-priority-preemption/), a
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key mechanism for Kubernetes to handle resource contention. The same page covers _eviction_,
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which is less relevant to the placeholder Pod approach, but is also a mechanism for Kubernetes
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to react when resources are contended.
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-->
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- 进一步了解 [PriorityClass](/zh-cn/docs/concepts/scheduling-eviction/pod-priority-preemption/#priorityclass)
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及其如何影响 Pod 调度。
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- 探索[节点自动扩缩容](/zh-cn/docs/concepts/cluster-administration/cluster-autoscaling/)，
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以基于工作负载需求动态调整集群的大小。
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- 了解 [Pod 抢占](/zh-cn/docs/concepts/scheduling-eviction/pod-priority-preemption/)，
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这是 Kubernetes 处理资源竞争的关键机制。这篇文档还涵盖了**驱逐**，
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虽然与占位 Pod 方法相关性较小，但也是 Kubernetes 在资源竞争时做出反应的一种机制。
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