使用自动伸缩需要先安装metrics-server mkdir -p kubernetes/HPA/ cd kubernetes/HPA/ wget https://github.com/kubernetes-incubator/metrics-server/archive/v0.3.1.zip unzip v0.3.1.zip cd metrics-server-0.3.1 ll deploy/1.8+/ -rw-r–r-- 1 root root 308 Sep 17 07:05 auth-delegator.yaml -rw-r–r-- 1 root root 329 Sep 17 07:05 auth-reader.yaml -rw-r–r-- 1 root root 298 Sep 17 07:05 metrics-apiservice.yaml -rw-r–r-- 1 root root 1098 Feb 13 02:02 metrics-server-deployment.yaml -rw-r–r-- 1 root root 249 Sep 17 07:05 metrics-server-service.yaml -rw-r–r-- 1 root root 612 Sep 17 07:05 resource-reader.yaml 修改deployment.yaml文件，修正集群问题
问题1：metrics-server默认使用节点hostname通过kubelet 10250端口获取数据，但是coredns里面没有该数据无法解析(10.96.0.10:53)，可以在metrics server启动命令添加参数 --kubelet-preferred-address-types=InternalIP 直接使用节点IP地址获取数据
问题2：kubelet 的10250端口使用的是https协议，连接需要验证tls证书。可以在metrics server启动命令添加参数–kubelet-insecure-tls不验证客户端证书
问题3：yaml文件中的image地址k8s.gcr.io/metrics-server-amd64:v0.3.0 需要梯子，需要改成中国可以访问的image地址，可以使用aliyun的。这里使用hub.docker.com里的google镜像地址 image: mirrorgooglecontainers/metrics-server-amd64:v0.3.1
针对以上3个问题修正后的部署文件内容如下：
cat deploy/1.8+/metrics-server-deployment.yaml
apiVersion: v1 kind: ServiceAccount metadata: name: metrics-server namespace: kube-system
apiVersion: extensions/v1beta1 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: k8s.gcr.io/metrics-server-amd64:v0.3.0 image: mirrorgooglecontainers/metrics-server-amd64:v0.3.1 imagePullPolicy: IfNotPresent command: - /metrics-server - --metric-resolution=30s - --kubelet-insecure-tls - --kubelet-preferred-address-types=InternalIP,Hostname,InternalDNS,ExternalDNS,ExternalIP volumeMounts: - name: tmp-dir mountPath: /tmp 部署启动metrics-server kubectl apply -f deploy/1.8+/ clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created serviceaccount/metrics-server created deployment.extensions/metrics-server created service/metrics-server created clusterrole.rbac.authorization.k8s.io/system:metrics-server created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created
kubectl get pods -n kube-system NAME READY STATUS RESTARTS AGE metrics-server-784fd5cdc5-2dgmx 1/1 Running 0 13s
查看集群节点资源使用情况（CPU，MEM） kubectl top nodes NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% ejucsmaster-shqs-1 585m 2% 9695Mi 7% ejucsmaster-shqs-2 300m 1% 8339Mi 6% ejucsmaster-shqs-3 340m 1% 8469Mi 6% ejucsnode-shqs-1 266m 1% 8580Mi 6% ejucsnode-shqs-2 127m 0% 8733Mi 6% ejucsnode-shqs-3 119m 0% 8558Mi 6%
安装完成以后每个pod需要配置CPU限制！！！ 如果不配置无法获取到数据
设置myreplicaset 最少1个，最多10个，cpu资源最多使用40% kubectl autoscale replicaset myreplicaset --min=1 --max=10 --cpu-percent=40 使用yaml方式部署 apiVersion: autoscaling/v1 kind: HorizontalPodAutoscaler metadata: name: test-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: test-app minReplicas: 1 #最小pod数 maxReplicas: 10 #最大pod数 targetCPUUtilizationPercentage: 70 #CPU占用百分比
