一、Kubernetes自动扩展器

• HPA：Pod 水平缩放器
• VPA：Pod 垂直缩放器
• CA：集群自动缩放器

1.1、Kubernetes Pod水平自动伸缩（HPA）

HPA官方文档 ：https://kubernetes.io/zh/docs/tasks/run-application/horizontal-pod-autoscale/

1.1.1、HPA简介

• HAP，全称 Horizontal Pod Autoscaler， 可以基于 CPU 利用率自动扩缩 ReplicationController、Deployment 和 ReplicaSet 中的 Pod 数量。 除了 CPU 利用率，也可以基于其他应程序提供的自定义度量指标来执行自动扩缩。 Pod 自动扩缩不适用于无法扩缩的对象，比如 DaemonSet。
• Pod 水平自动扩缩特性由 Kubernetes API 资源和控制器实现。资源决定了控制器的行为。 控制器会周期性的调整副本控制器或 Deployment 中的副本数量，以使得 Pod 的平均 CPU 利用率与用户所设定的目标值匹配。

• HPA 定期检查内存和 CPU 等指标，自动调整 Deployment 中的副本数，比如流量变化：

---

• 实际生产中，广泛使用这四类指标：
  • 1、Resource metrics - CPU核内存利用率指标
  • 2、Pod metrics - 例如网络利用率和流量
  • 3、Object metrics - 特定对象的指标，比如Ingress, 可以按每秒使用请求数来扩展容器
  • 4、Custom metrics - 自定义监控，比如通过定义服务响应时间，当响应时间达到一定指标时自动扩容

1.1.2、HPA示例

• 1、首先我们部署一个nginx,副本数为2，请求cpu资源为200m。同时为了便宜测试，使用NodePort暴露服务，命名空间设置为:hpa

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: nginx
  name: nginx
  namespace: hpa
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx
        name: nginx
        resources:
          requests:
            cpu: 200m
            memory: 100Mi
---
apiVersion: v1
kind: Service
metadata:
  name: nginx
  namespace: hpa
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 80
  selector:
    app: nginx

• 2、查看部署结果

# kubectl  get po -n hpa
  NAME                     READY   STATUS    RESTARTS   AGE
  nginx-5c87768612-48b4v   1/1     Running   0          8m38s
  nginx-5c87768612-kfpkq   1/1     Running   0          8m38s

• 3、创建HPA
  • 这里创建一个HPA，用于控制我们上一步骤中创建的 Deployment，使 Pod 的副本数量维持在 1 到 10 之间。
  • HPA 将通过增加或者减少 Pod 副本的数量（通过 Deployment）以保持所有 Pod 的平均 CPU 利用率在 50% 以内。

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: nginx
  namespace: hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: nginx
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

• 4、查看部署结果

# kubectl  get hpa -n hpa
  NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
  nginx   Deployment/nginx   0%/50%      1                10                 2          50s

• 5、压测观察Pod数和HPA变化

# 执行压测命令
# ab -c 1000 -n 100000000 http://127.0.0.1:30792/
  This is ApacheBench, Version 2.3 <$Revision: 1843412 $>
  Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
  Licensed to The Apache Software Foundation, http://www.apache.org/
  Benchmarking 127.0.0.1 (be patient)

# 观察变化
#  kubectl  get hpa -n hpa
  NAME    REFERENCE          TARGETS    MINPODS   MAXPODS   REPLICAS   AGE
  nginx   Deployment/nginx   303%/50%   1         10        7          12m

# kubectl  get po -n hpa
  NAME                         READY   STATUS    RESTARTS   AGE
  pod/nginx-5c87768612-6b4sl   1/1     Running   0          85s
  pod/nginx-5c87768612-99mjb   1/1     Running   0          69s
  pod/nginx-5c87768612-cls7r   1/1     Running   0          85s
  pod/nginx-5c87768612-hhdr7   1/1     Running   0          69s
  pod/nginx-5c87768612-jj744   1/1     Running   0          85s
  pod/nginx-5c87768612-kfpkq   1/1     Running   0          27m
  pod/nginx-5c87768612-xb94x   1/1     Running   0          69s

• 6、可以看出，hpa TARGETS达到了303%，需要扩容。pod数自动扩展到了7个。等待压测结束；

# kubectl get hpa -n hpa
NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
nginx   Deployment/nginx   20%/50%   1         10        7          16m

---N分钟后---

# kubectl get hpa -n hpa
  NAME    REFERENCE          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
  nginx   Deployment/nginx   0%/50%    1         10        7          18m

---再过N分钟后---

# kubectl  get po -n hpa
  NAME                     READY   STATUS    RESTARTS   AGE
  nginx-5c87768612-jj744   1/1     Running   0          11m

• 7、hpa示例总结
  • CPU 利用率已经降到 0，所以 HPA 将自动缩减副本数量至 1。
  • 为什么会将副本数降为1，而不是我们部署时指定的replicas: 2呢？
    • 因为在创建HPA时，指定了副本数范围，这里是minReplicas: 1，maxReplicas: 10。所以HPA在缩减副本数时减到了1。

1.2、Kubernetes Pod垂直自动伸缩（VPA）

VPA项目托管地址 ：https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler

1.2.1、VPA 简介

• VPA 全称 Vertical Pod Autoscaler，即垂直 Pod 自动扩缩容，它根据容器资源使用率自动设置 CPU 和 内存 的requests，从而允许在节点上进行适当的调度，以便为每个 Pod 提供适当的资源。
• 它既可以缩小过度请求资源的容器，也可以根据其使用情况随时提升资源不足的容量。

• 有些时候无法通过增加 Pod 数来扩容，比如数据库。这时候可以通过 VPA 增加 Pod 的大小，比如调整 Pod 的 CPU 和内存：

1.2.2、VPA示例

参考博文 ：https://www.jianshu.com/p/94ea8bee433e

1.2.2.1、部署metrics-server

• 1、下载部署清单文件

# wget  https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.7/components.yaml

• 2、修改components.yaml文件
  • 修改了镜像地址，gcr.io为我自己的仓库
  • 修改了metrics-server启动参数args，要不然会报错unable to fully scrape metrics from source kubelet_summary…

- name: metrics-server
        image: scofield/metrics-server:v0.3.7
        imagePullPolicy: IfNotPresent
        args:
          - --cert-dir=/tmp
          - --secure-port=4443
          - /metrics-server
          - --kubelet-insecure-tls
          - --kubelet-preferred-address-types=InternalIP

• 3、部署及验证

# kubectl  apply -f components.yaml

# kubectl  get po -n kube-system
  NAME                                       READY   STATUS    RESTARTS   AGE
  metrics-server-7947cb98b6-xw6b8            1/1     Running   0          10m
# kubectl  top nodes

1.2.2.2、部署vertical-pod-autoscaler

• 1、克隆autoscaler

# git clone https://github.com/kubernetes/autoscaler.git

• 2、部署autoscaler

#  cd autoscaler/vertical-pod-autoscaler
#  ./hack/vpa-up.sh
  Warning: apiextensions.k8s.io/v1beta1 CustomResourceDefinition is deprecated in v1.16+, unavailable in v1.22+; use apiextensions.k8s.io/v1 CustomResourceDefinition
  customresourcedefinition.apiextensions.k8s.io/verticalpodautoscalers.autoscaling.k8s.io created
  customresourcedefinition.apiextensions.k8s.io/verticalpodautoscalercheckpoints.autoscaling.k8s.io created
  clusterrole.rbac.authorization.k8s.io/system:metrics-reader created
  clusterrole.rbac.authorization.k8s.io/system:vpa-actor created
  clusterrole.rbac.authorization.k8s.io/system:vpa-checkpoint-actor created
  clusterrole.rbac.authorization.k8s.io/system:evictioner created
  clusterrolebinding.rbac.authorization.k8s.io/system:metrics-reader created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-actor created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-checkpoint-actor created
  clusterrole.rbac.authorization.k8s.io/system:vpa-target-reader created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-target-reader-binding created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-evictionter-binding created
  serviceaccount/vpa-admission-controller created
  clusterrole.rbac.authorization.k8s.io/system:vpa-admission-controller created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-admission-controller created
  clusterrole.rbac.authorization.k8s.io/system:vpa-status-reader created
  clusterrolebinding.rbac.authorization.k8s.io/system:vpa-status-reader-binding created
  serviceaccount/vpa-updater created
  deployment.apps/vpa-updater created
  serviceaccount/vpa-recommender created
  deployment.apps/vpa-recommender created
  Generating certs for the VPA Admission Controller in /tmp/vpa-certs.
  Generating RSA private key, 2048 bit long modulus (2 primes)
  ............................................................................+++++
  .+++++
  e is 65537 (0x010001)
  Generating RSA private key, 2048 bit long modulus (2 primes)
  ............+++++
  ...........................................................................+++++
  e is 65537 (0x010001)
  Signature ok
  subject=CN = vpa-webhook.kube-system.svc
  Getting CA Private Key
  Uploading certs to the cluster.
  secret/vpa-tls-certs created
  Deleting /tmp/vpa-certs.
  deployment.apps/vpa-admission-controller created
  service/vpa-webhook created

• 3、验证部署结果

# 可以看到metrics-server和vpa都已经正常运行了

# kubectl  get po -n kube-system
  NAME                                        READY   STATUS    RESTARTS   AGE
  metrics-server-7947cb98b6-xw6b8             1/1     Running   0          46m
  vpa-admission-controller-7d87559549-g77h9   1/1     Running   0          10m
  vpa-recommender-84bf7fb9db-65669            1/1     Running   0          10m
  vpa-updater-79cc46c7bb-5p889                1/1     Running   0          10m

1.2.2.3、updateMode: “Off”（此模式仅获取资源推荐不更新Pod）

• 1、部署一个nginx服务,部署到namespace: vpa

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: nginx
  name: nginx
  namespace: vpa
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx
        name: nginx
        resources:
          requests:
            cpu: 100m
            memory: 250Mi

• 2、创建一个NodePort类型的service，便于压测Pod

# cat  nginx-vpa-ingress.yaml
apiVersion: v1
kind: Service
metadata:
  name: nginx
  namespace: vpa
spec:
  type: NodePort
  ports:
  - port: 80
    targetPort: 80
  selector:
    app: nginx

# kubectl  get svc -n vpa
  NAME    TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
  nginx   NodePort   10.97.250.131   <none>        80:32621/TCP   55s

• 3、创建VPA
  • 这里先使用updateMode: "Off"模式，这种模式仅获取资源推荐不更新Pod

# cat   nginx-vpa-demo.yaml
apiVersion: autoscaling.k8s.io/v1beta2
kind: VerticalPodAutoscaler
metadata:
  name: nginx-vpa
  namespace: vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: nginx
  updatePolicy:
    updateMode: "Off"
  resourcePolicy:
    containerPolicies:
    - containerName: "nginx"
      minAllowed:
        cpu: "250m"
        memory: "100Mi"
      maxAllowed:
        cpu: "2000m"
        memory: "2048Mi"
4、查看部署结果

[root@k8s-node001 examples]# kubectl  get vpa -n vpa
NAME        AGE
nginx-vpa   2m34s
5、使用describe查看vpa详情，主要关注Container Recommendations

[root@k8s-node001 examples]# kubectl  describe  vpa nginx-vpa   -n vpa
Name:         nginx-vpa
Namespace:    vpa
....略去10000字 哈哈......
  Update Policy:
    Update Mode:  Off
Status:
  Conditions:
    Last Transition Time:  2020-09-28T04:04:25Z
    Status:                True
    Type:                  RecommendationProvided
  Recommendation:
    Container Recommendations:
      Container Name:  nginx
      Lower Bound:
        Cpu:     250m
        Memory:  262144k
      Target:
        Cpu:     250m
        Memory:  262144k
      Uncapped Target:
        Cpu:     25m
        Memory:  262144k
      Upper Bound:
        Cpu:     803m
        Memory:  840190575
Events:          <none>

Lower Bound:                 下限值
Target:                      推荐值
Upper Bound:                 上限值
Uncapped Target:           如果没有为VPA提供最小或最大边界，则表示目标利用率
上述结果表明，推荐的 Pod 的 CPU 请求为 25m，推荐的内存请求为 262144k 字节。

• 4、对nginx进行压测，执行压测命令

# ab -c 100 -n 10000000 http://192.168.127.124:32621/
  This is ApacheBench, Version 2.3 <$Revision: 1843412 $>
  Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
  Licensed to The Apache Software Foundation, http://www.apache.org/

  Benchmarking 192.168.127.124 (be patient)
  Completed 1000000 requests
  Completed 2000000 requests
  Completed 3000000 requests

• 5、稍后再观察VPA Recommendation变化

# kubectl  describe  vpa nginx-vpa   -n vpa |tail -n 20 
  Conditions:
    Last Transition Time:  2021-06-28T04:04:25Z
    Status:                True
    Type:                  RecommendationProvided
  Recommendation:
    Container Recommendations:
      Container Name:  nginx
      Lower Bound:
        Cpu:     250m
        Memory:  262144k
      Target:
        Cpu:     476m
        Memory:  262144k
      Uncapped Target:
        Cpu:     476m
        Memory:  262144k
      Upper Bound:
        Cpu:     2
        Memory:  387578728
Events:          <none>

• 从输出信息可以看出，VPA对Pod给出了推荐值：Cpu: 476m，因为我们这里设置了updateMode: “Off”，所以不会更新Pod;

1.2.2.4、updateMode: “Auto”（此模式当目前运行的pod的资源达不到VPA的推荐值，就会执行pod驱逐，重新部署新的足够资源的服务）

• 1、把updateMode: “Auto”，看看VPA会有什么动作
  • 并且把resources改为：memory: 50Mi，cpu: 100m

# kubectl  apply -f nginx-vpa.yaml
  deployment.apps/nginx created

# cat nginx-vpa.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: nginx
  name: nginx
  namespace: vpa
spec:
  replicas: 2
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx
        name: nginx
        resources:
          requests:
            cpu: 100m
            memory: 50Mi

# kubectl  get po  -n vpa
  NAME                     READY   STATUS    RESTARTS   AGE
  nginx-7ff65f974c-f4vgl   1/1     Running   0          114s
  nginx-7ff65f974c-v9ccx   1/1     Running   0          114s

• 2、再次部署vpa,这里VPA部署文件nginx-vpa-demo.yaml只改了updateMode: "Auto"和name: nginx-vpa-2

# cat  nginx-vpa-demo.yaml
apiVersion: autoscaling.k8s.io/v1beta2
kind: VerticalPodAutoscaler
metadata:
  name: nginx-vpa-2
  namespace: vpa
spec:
  targetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: nginx
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: "nginx"
      minAllowed:
        cpu: "250m"
        memory: "100Mi"
      maxAllowed:
        cpu: "2000m"
        memory: "2048Mi"

# kubectl apply -f nginx-vpa-demo.yaml
  verticalpodautoscaler.autoscaling.k8s.io/nginx-vpa created

# kubectl  get vpa -n vpa
  NAME        AGE
  nginx-vpa-2   9s

• 3、再次压测

# ab -c 1000 -n 100000000 http://192.168.127.124:32621/

• 4、稍后使用describe查看vpa详情，同样只关注Container Recommendations

# kubectl  describe  vpa nginx-vpa-2    -n vpa |tail -n 30
      Min Allowed:
        Cpu:     250m
        Memory:  100Mi
  Target Ref:
    API Version:  apps/v1
    Kind:         Deployment
    Name:         nginx
  Update Policy:
    Update Mode:  Auto
Status:
  Conditions:
    Last Transition Time:  2021-06-28T04:48:25Z
    Status:                True
    Type:                  RecommendationProvided
  Recommendation:
    Container Recommendations:
      Container Name:  nginx
      Lower Bound:
        Cpu:     250m
        Memory:  262144k
      Target:
        Cpu:     476m
        Memory:  262144k
      Uncapped Target:
        Cpu:     476m
        Memory:  262144k
      Upper Bound:
        Cpu:     2
        Memory:  262144k
Events:          <none>

• Target变成了Cpu: 587m ，Memory: 262144k

• 5、查看event事件

~]# kubectl  get event -n vpa
  LAST SEEN   TYPE      REASON              OBJECT                        MESSAGE
  33m         Normal    Pulling             pod/nginx-7ff65f974c-f4vgl    Pulling image "nginx"
  33m         Normal    Pulled              pod/nginx-7ff65f974c-f4vgl    Successfully pulled image "nginx" in 15.880996269s
  33m         Normal    Created             pod/nginx-7ff65f974c-f4vgl    Created container nginx
  33m         Normal    Started             pod/nginx-7ff65f974c-f4vgl    Started container nginx
  26m         Normal    EvictedByVPA        pod/nginx-7ff65f974c-f4vgl    Pod was evicted by VPA Updater to apply resource recommendation.
  26m         Normal    Killing             pod/nginx-7ff65f974c-f4vgl    Stopping container nginx
  35m         Normal    Scheduled           pod/nginx-7ff65f974c-hnzr5    Successfully assigned vpa/nginx-7ff65f974c-hnzr5 to k8s-node005
  35m         Normal    Pulling             pod/nginx-7ff65f974c-hnzr5    Pulling image "nginx"
  34m         Normal    Pulled              pod/nginx-7ff65f974c-hnzr5    Successfully pulled image "nginx" in 40.750855715s
  34m         Normal    Scheduled           pod/nginx-7ff65f974c-v9ccx    Successfully assigned vpa/nginx-7ff65f974c-v9ccx to k8s-node004
  33m         Normal    Pulling             pod/nginx-7ff65f974c-v9ccx    Pulling image "nginx"
  33m         Normal    Pulled              pod/nginx-7ff65f974c-v9ccx    Successfully pulled image "nginx" in 15.495315629s
  33m         Normal    Created             pod/nginx-7ff65f974c-v9ccx    Created container nginx
  33m         Normal    Started             pod/nginx-7ff65f974c-v9ccx    Started container nginx

• 从输出信息可以了解到，vpa执行了EvictedByVPA，自动停掉了nginx，然后使用 VPA推荐的资源启动了新的nginx，我们查看下nginx的pod可以得到确认;

~]# kubectl  describe po nginx-7ff65f974c-2m9zl -n vpa
Name:         nginx-7ff65f974c-2m9zl
Namespace:    vpa
Priority:     0
Node:         k8s-node004/192.168.100.184
Start Time:   June, 28 Sep 2021 00:46:19 -0400
Labels:       app=nginx
              pod-template-hash=7ff65f974c
Annotations:  cni.projectcalico.org/podIP: 100.67.191.53/32
              vpaObservedContainers: nginx
              vpaUpdates: Pod resources updated by nginx-vpa: container 0: cpu request, memory request
Status:       Running
IP:           100.67.191.53
IPs:
  IP:           100.67.191.53
Controlled By:  ReplicaSet/nginx-7ff65f974c
Containers:
  nginx:
    Container ID:   docker://c96bcd07f35409d47232a0bf862a76a56352bd84ef10a95de8b2e3f6681df43d
    Image:          nginx
    Image ID:       docker-pullable://nginx@sha256:c628b67d21744fce822d22fdcc0389f6bd763daac23a6b77147d0712ea7102d0
    Port:           <none>
    Host Port:      <none>
    State:          Running
      Started:      June, 28 Sep 2021 00:46:38 -0400
    Ready:          True
    Restart Count:  0
    Requests:
      cpu:        476m
      memory:     262144k

• 看重点Requests：cpu: 476m，memory: 262144k
• 再回头看看部署文件

          requests:
            cpu: 100m
            memory: 50Mi

• 随着服务的负载的变化，VPA的推荐值也会不断变化。当目前运行的pod的资源达不到VPA的推荐值，就会执行pod驱逐，重新部署新的足够资源的服务。

1.2.2.5、VPA使用限制&优势

• 限制
  • 不能与HPA（Horizontal Pod Autoscaler ）一起使用；
• 优势
  • Pod 资源用其所需，所以集群节点使用效率高；
  • Pod 会被安排到具有适当可用资源的节点上；
  • 不必运行基准测试任务来确定 CPU 和内存请求的合适值；
  • VPA 可以随时调整 CPU 和内存请求，无需人为操作，因此可以减少维护时间；

1.3、Kubernetes 集群自动缩放器（CA）

CA项目托管地址 ：https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler

节点的初始化: https://kubernetes.io/docs/reference/command-line-tools-reference/kubelet-tls-bootstrapping/

1.3.1、CA简介

• 集群自动伸缩器（CA）基于待处理的豆荚扩展集群节点。它会定期检查是否有任何待处理的豆荚，如果需要更多的资源，并且扩展的集群仍然在用户提供的约束范围内，则会增加集群的大小。CA与云供应商接口，请求更多节点或释放空闲节点。它与GCP、AWS和Azure兼容。版本1.0（GA）与Kubernetes 1.8一起发布。

• 当集群资源不足时，CA 会自动配置新的计算资源并添加到集群中：

1.4、Pod 自动缩放的前置时间

参考博文 ：https://mp.weixin.qq.com/s/GKS3DJHm4p0Tjtj8nJRGmA

• 四个因素：
  • 1.HPA 的响应耗时
  • 2.CA 的响应耗时
  • 3.节点的初始化耗时
  • 4.Pod 的创建时间

---

• 默认情况下，kubelet 每 10 秒抓取一次 Pod 的 CPU 和内存占用情况；
• 每分钟，Metrics Server 会将聚合的指标开放给 Kubernetes API 的其他组件使用；

• CA 每 10 秒排查不可调度的 Pod。[10]
  • 少于 100 个节点，且每个节点最多 30 个 Pod，时间不超过 30s。平均延迟大约 5s；
  • 100 到 1000个节点，不超过 60s。平均延迟大约 15s；

• 节点的配置时间，取决于云服务商。通常在 3~5 分钟；

• 容器运行时创建 Pod：启动容器的几毫秒和下载镜像的几秒钟。如果不做镜像缓存，几秒到 1 分钟不等，取决于层的大小和梳理；

• 对于小规模的集群，最坏的情况是 6 分 30 秒。对于 100 个以上节点规模的集群，可能高达 7 分钟；

HPA delay:          1m30s +
CA delay:           0m30s +
Cloud provider:     4m    +
Container runtime:  0m30s +
=========================
Total               6m30s

• 突发情况，比如流量激增，你是否愿意等这 7 分钟？该如何压缩时间？(即使调小了上述设置，依然会受云服务商的时间限制)

HPA 的刷新时间，默认 15 秒，通过 --horizontal-pod-autoscaler-sync-period 标志控制;

Metrics Server 的指标抓取时间，默认 60 秒，通过 metric-resolution 控

CA 的扫描间隔，默认 10 秒，通过 scan-interval 控制;

节点上缓存镜像，比如 kube-fledged等工具;