k8s之HPA（Pod水平自动伸缩）

2023-12-14 13:06| 来源: 网络整理| 查看: 265

Horizontal Pod Autoscaler官方文档：Pod 水平自动扩缩 | Kubernetes

Pod 水平自动扩缩（Horizontal Pod Autoscaler）可以基于 CPU 利用率自动扩缩 ReplicationController、Deployment、ReplicaSet 和 StatefulSet 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。控制器会周期性地调整副本控制器或 Deployment 中的副本数量，以使得类似 Pod 平均 CPU 利用率、平均内存利用率这类观测到的度量值与用户所设定的目标值匹配。

Kubectl top ->apiserver->metrics server-> kubelet(cadvisor)->pod

HPA是根据指标来进行自动伸缩的，目前HPA有两个版本–v1和v2beta

HPA的API有三个版本，通过kubectl api-versions | grep autoscal可看到

[root@master1 yaml]# kubectl api-versions | grep autosca autoscaling/v1 autoscaling/v2beta1 autoscaling/v2beta2

查看使用的版本：

kubectl explain hpa

查看指定其他版本：

kubectl explain hpa --api-version=autoscaling/v2beta1

autoscaling/v1只支持基于CPU指标的缩放；

autoscaling/v2beta1支持Resource Metrics（资源指标，如pod内存）和Custom Metrics（自定义指标）的缩放；

autoscaling/v2beta2支持Resource Metrics（资源指标，如pod的内存）和Custom Metrics（自定义指标）和ExternalMetrics

1.部署一下metrics-server，收集集群资源利用率

metrics-server版本获取： https://github.com/kubernetes-sigs/metrics-server/releases

vim metrics-server.yaml

apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: system:aggregated-metrics-reader labels: rbac.authorization.k8s.io/aggregate-to-view: "true" rbac.authorization.k8s.io/aggregate-to-edit: "true" rbac.authorization.k8s.io/aggregate-to-admin: "true" rules: - apiGroups: ["metrics.k8s.io"] resources: ["pods", "nodes"] verbs: ["get", "list", "watch"] --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: metrics-server:system:auth-delegator roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:auth-delegator subjects: - kind: ServiceAccount name: metrics-server namespace: kube-system --- apiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: metrics-server-auth-reader namespace: kube-system roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: extension-apiserver-authentication-reader subjects: - kind: ServiceAccount name: metrics-server namespace: kube-system --- apiVersion: apiregistration.k8s.io/v1 kind: APIService metadata: name: v1beta1.metrics.k8s.io spec: service: name: metrics-server namespace: kube-system group: metrics.k8s.io version: v1beta1 insecureSkipTLSVerify: true groupPriorityMinimum: 100 versionPriority: 100 --- apiVersion: v1 kind: ServiceAccount metadata: name: metrics-server namespace: kube-system --- apiVersion: apps/v1 kind: Deployment metadata: name: metrics-server namespace: kube-system labels: k8s-app: metrics-server spec: selector: matchLabels: k8s-app: metrics-server template: metadata: name: metrics-server labels: k8s-app: metrics-server spec: serviceAccountName: metrics-server volumes: # mount in tmp so we can safely use from-scratch images and/or read-only containers - name: tmp-dir emptyDir: {} containers: - name: metrics-server image: registry.cn-shenzhen.aliyuncs.com/lishanbin/metrics-server:v0.3.7 imagePullPolicy: IfNotPresent args: - --cert-dir=/tmp - --secure-port=4443 - --kubelet-insecure-tls - --kubelet-preferred-address-types=InternalIP ports: - name: main-port containerPort: 4443 protocol: TCP securityContext: readOnlyRootFilesystem: true runAsNonRoot: true runAsUser: 1000 volumeMounts: - name: tmp-dir mountPath: /tmp #nodeSelector: # kubernetes.io/os: linux # kubernetes.io/arch: "amd64" --- apiVersion: v1 kind: Service metadata: name: metrics-server namespace: kube-system labels: kubernetes.io/name: "Metrics-server" kubernetes.io/cluster-service: "true" spec: selector: k8s-app: metrics-server ports: - port: 443 protocol: TCP targetPort: main-port --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: system:metrics-server rules: - apiGroups: - "" resources: - pods - nodes - nodes/stats - namespaces - configmaps verbs: - get - list - watch --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: system:metrics-server roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: system:metrics-server subjects: - kind: ServiceAccount name: metrics-server namespace: kube-system [root@master1 yaml]# kubectl api-versions |grep metrics metrics.k8s.io/v1beta1 [root@master1 yaml]# kubectl top nodes NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% 192.168.203.219 180m 2% 2936Mi 18% 192.168.203.220 499m 6% 1662Mi 10% 192.168.203.221 203m 2% 1883Mi 11% 192.168.203.223 151m 1% 9805Mi 61% 192.168.203.226 343m 4% 10645Mi 67% 192.168.203.228 299m 3% 10698Mi 67%

2.hpa基于cpu自动扩缩容

HPA伸缩过程：收集HPA控制下所有Pod最近的cpu使用情况（CPU utilization）对比在扩容条件里记录的cpu限额（CPUUtilization）调整实例数（必须要满足不超过最大/最小实例数）每隔30s做一次自动扩容的判断 CPU utilization的计算方法是用cpu usage（最近一分钟的平均值，通过metrics可以直接获取到）除以cpu request（这里cpu request就是我们在创建容器时制定的cpu使用核心数）得到一个平均值，这个平均值可以理解为：平均每个Pod CPU核心的使用占比。

HPA进行伸缩算法：计算公式：TargetNumOfPods = ceil(sum(CurrentPodsCPUUtilization) / Target) ceil()表示取大于或等于某数的最近一个整数每次扩容后冷却3分钟才能再次进行扩容，而缩容则要等5分钟后。当前Pod Cpu使用率与目标使用率接近时，不会触发扩容或缩容：触发条件：avg(CurrentPodsConsumption) / Target >1.1 或

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