pod – Linux系统运维日志

概念

HPA是kubernetes里面pod弹性伸缩的实现,它能根据设置的监控阀值进行pod的弹性扩缩容，目前默认HPA只能支持cpu和内存的阀值检测扩缩容，但也可以通过custom metric api 调用prometheus实现自定义metric 来更加灵活的监控指标实现弹性伸缩。但hpa不能用于伸缩一些无法进行缩放的控制器如DaemonSet。这里我们用的是resource metric api.

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实现hpa的两大关键

1、监控指标的获取
早期kubernetes版本是使用hepster，在1.10后面版本更推荐使用metric-server

hepster简单来说是api-server获取节点信息，然后通过kubelet获取监控信息，因为kubelet内置了cadvisor。

metric-server，简单来说是通过metric-api来获取节点信息和监控信息。https://github.com/kubernetes-incubator/metrics-server

2、伸缩判定算法
HPA通过定期（定期轮询的时间通过–horizontal-pod-autoscaler-sync-period选项来设置，默认的时间为30秒）查询pod的状态，获得pod的监控数据。然后，通过现有pod的使用率的平均值跟目标使用率进行比较。
pod的使用率的平均值：
监控资源1分钟使用的平均值/设定的每个Pod的request资源值

扩容的pod数计算公式

TargetNumOfPods = ceil(sum(CurrentPodsCPUUtilization) / Target)

celi函数作用：
返回大于或者等于指定表达式的最小整数

在每次扩容和缩容时都有一个窗口时间，在执行伸缩操作后，在这个窗口时间内，不会在进行伸缩操作，可以理解为类似等一下放技能的冷却时间。默认扩容为3分钟(–horizontal-pod-autoscaler-upscale-delay)，缩容为5分钟(–horizontal-pod-autoscaler-downscale-delay)。另外还需要以下情况下才会进行任何缩放avg（CurrentPodsConsumption）/ Target下降9%，进行缩容，增加至10%进行扩容。以上两条件需要都满足。

这样做好处是：
1、判断的精度高，不会频繁的扩缩pod，造成集群压力大。
2、避免频繁的扩缩pod，防止应用访问不稳定。

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实现hpa的条件：
1、hpa不能autoscale daemonset类型control
2、要实现autoscale，pod必须设置request

配置HPA

这里以kubeadm 部署和的kubernetes 1.11和Rancher2.0部署的kubernetes 1.10为例
环境信息

操作系统：ubuntu16.04
kubernetes版本：1.11
rancher：2.0.6

kubeadm方式

将metric-server从github拉取下来

git clone [email protected]:kubernetes-incubator/metrics-server.git

早期kubelet的10255端口是开放，但后面由于10255是一个非安全的端口容易被入侵，所以被关闭了。metric-server默认是从kubelet的10255端口去拉取监控信息的，所以这里需要修改从10250去拉取

edit  metrics-server/deploy/1.8+/metrics-server-deployment.yaml

修改source为以下内容。

–source=kubernetes.summary_api:https://kubernetes.default?kubeletHttps=true&kubeletPort=10250&insecure=true

apply yaml文件

kubectl apply -f metrics-server/deploy/1.8+/.1

![](http://ohx02qrb8.bkt.clouddn.com/kube_hpa_3.png)
![](http://ohx02qrb8.bkt.clouddn.com/kube_hpa_4.png)
![](http://ohx02qrb8.bkt.clouddn.com/kube_hpa_5.png)
等待一分钟  
执行

kubect top node

kubectl top pods

查看pod和node监控信息

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创建个一个deployment，配置hpa测试

apiVersion: v1
kind: Service
metadata:
  name: podinfo
  labels:
    app: podinfo
spec:
  type: NodePort
  ports:
    - port: 9898
      targetPort: 9898
      nodePort: 31198
      protocol: TCP
  selector:
    app: podinfo
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: podinfo
spec:
  replicas: 2
  template:
    metadata:
      labels:
        app: podinfo
      annotations:
        prometheus.io/scrape: 'true'
    spec:
      containers:
      - name: podinfod
        image: stefanprodan/podinfo:0.0.1
        imagePullPolicy: Always
        command:
          - ./podinfo
          - -port=9898
          - -logtostderr=true
          - -v=2
        volumeMounts:
          - name: metadata
            mountPath: /etc/podinfod/metadata
            readOnly: true
        ports:
        - containerPort: 9898
          protocol: TCP
        resources:
          requests:
            memory: "32Mi"
            cpu: "1m"
          limits:
            memory: "256Mi"
            cpu: "100m"
      volumes:
        - name: metadata
          downwardAPI:
            items:
              - path: "labels"
                fieldRef:
                  fieldPath: metadata.labels
              - path: "annotations"
                fieldRef:
                  fieldPath: metadata.annotations

apply yaml文件

配置hpa

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: podinfo
spec:
  scaleTargetRef:
    apiVersion: extensions/v1beta1
    kind: Deployment
    name: podinfo
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 80
  - type: Resource
    resource:
      name: memory
      targetAverageValue: 200Mi

apply yaml文件

get hpa

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测试
使用webbench进行压力测试。

wget http://home.tiscali.cz/~cz210552/distfiles/webbench-1.5.tar.gz

tar -xvf webbench-1.5.tar.gz

cd webbench-1.5/
make && make install

webbench -c 1000 -t 12360 http://172.31.164.104:31198/

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可以看见随着cpu压力的增加，已经自动scale了，需要注意的是，scale up是一个阶段性的过程，并不是一次性就直接scale到max了，而是一个阶段性的过程，判定算法就是上文介绍的内容。
隔断时间没操作压力下来后，自动缩减pod

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Rancher2.0

使用rancher2.0部署的kubernetes如何开启HPA，因为rancher2.0将所有的kubernetes组件都容器化了，所以有些地方需要配置
配置方法
rancher2.0部署的kubernetes默认没有配置Metrics API aggregator的证书所以需要手动生成
生成证书文件

cat >> /etc/kubernetes/ssl/ca-config.json<< EOF
{
  "signing": {
    "default": {
      "expiry": "87600h"
    },
    "profiles": {
      "kubernetes": {
        "usages": [
            "signing",
            "key encipherment",
            "server auth",
            "client auth"
        ],
        "expiry": "87600h"
      }
    }
  }
}
EOF

wget https://pkg.cfssl.org/R1.2/cfssl_linux-amd64
wget https://pkg.cfssl.org/R1.2/cfssljson_linux-amd64
mv cfssljson_linux-amd64  /usr/bin/cfssljson
mv cfssl_linux-amd64  /usr/bin/cfssl
chmod a+x  /usr/bin/cfssljson
chmod a+x  /usr/bin/cfssl

cd /etc/kubernetes/ssl/
cat >> front-proxy-ca-csr.json <<EOF
{
    "CN": "kubernetes",
    "key": {
        "algo": "rsa",
        "size": 2048
    }
}
EOF

cat >>front-proxy-client-csr.json<<EOF
{
    "CN": "front-proxy-client",
        "algo": "rsa",
        "size": 2048
    }
}
EOF

cfssl gencert   -initca front-proxy-ca-csr.json |

cfssljson -bare front-proxy-ca

cfssl gencert   -ca=front-proxy-ca.pem  
  -ca-key=front-proxy-ca-key.pem 
-config=/etc/kubernetes/ssl/ca-config.json   
-profile=kubernetes   
front-proxy-client-csr.json | cfssljson -bare front-proxy-client

拷贝证书导集群其他节点

scp /etc/kubernetes/ssl/front-*

修改api-server的启动参数

--requestheader-client-ca-file=/etc/kubernetes/ssl/front-proxy-ca.pem 
--requestheader-allowed-names=front-proxy-client
--proxy-client-cert-file=/etc/kubernetes/ssl/front-proxy-client.pem
--proxy-client-key-file=/etc/kubernetes/ssl/front-proxy-client-key.pem

rancher2.0 custom集群时需要rke的启动修改参数，来修改api-server启动参数

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view in API—>edit

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将其部署方式的value设置为null

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修改rancherKubernetesEngineConfig
这里面内容是json的，可以复制出来通过json解析工具解析

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我们需要添加的参数
api-server配置

--requestheader-client-ca-file=/etc/kubernetes/ssl/front-proxy-ca.pem 
--requestheader-allowed-names=front-proxy-client
--proxy-client-cert-file=/etc/kubernetes/ssl/front-proxy-client.pem
--proxy-client-key-file=/etc/kubernetes/ssl/front-proxy-client-key.pem

json文件

{"addonJobTimeout":30,"authentication":{"strategy":"x509","type":"/v3/schemas/authnConfig"},"authorization":{"type":"/v3/schemas/authzConfig"},"bastionHost":{"sshAgentAuth":false,"type":"/v3/schemas/bastionHost"},"cloudProvider":{"type":"/v3/schemas/cloudProvider"},"ignoreDockerVersion":true,"ingress":{"provider":"nginx","type":"/v3/schemas/ingressConfig"},"kubernetesVersion":"v1.10.1-rancher1","network":{"canalNetworkProvider":{"iface":"","type":"/v3/schemas/canalNetworkProvider"},"plugin":"canal","type":"/v3/schemas/networkConfig"},"services":{"etcd":{"extraArgs":{"election-timeout":"5000","heartbeat-interval":"500"},"snapshot":false,"type":"/v3/schemas/etcdService"},"kubeApi":{"extraArgs":{"enable-aggregator-routing":"true","proxy-client-cert-file":"/etc/kubernetes/ssl/front-proxy-client.pem","proxy-client-key-file":"/etc/kubernetes/ssl/front-proxy-client-key.pem","requestheader-allowed-names":"front-proxy-client","requestheader-client-ca-file":"/etc/kubernetes/ssl/front-proxy-ca.pem","requestheader-extra-headers-prefix":"X-Remote-Extra-","requestheader-group-headers":"X-Remote-Group","requestheader-username-headers":"X-Remote-User"},"podSecurityPolicy":false,"type":"/v3/schemas/kubeAPIService"},"kubeController":{"type":"/v3/schemas/kubeControllerService"},"kubelet":{"extraArgs":{"anonymous-auth":"true","read-only-port":"10255"},"failSwapOn":false,"type":"/v3/schemas/kubeletService"},"kubeproxy":{"type":"/v3/schemas/kubeproxyService"},"scheduler":{"type":"/v3/schemas/schedulerService"},"type":"/v3/schemas/rkeConfigServices"},"sshAgentAuth":false,"type":"/v3/schemas/rancherKubernetesEngineConfig"}

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然后集群会出现update状态，会将api-server和kubelt滚动更新

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部署metric-server
修改yaml文件

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
      containers:
      - name: metrics-server
        image: wanshaoyuan/metric-server:v1.0
        imagePullPolicy: Always
        command:
        - /metrics-server
        - --source=kubernetes.summary_api:https://kubernetes.default?kubeletHttps=true&kubeletPort=10250&insecure=true

部署

kubectl apply -f /root/k8s-prom-hpa/metrics-server/.

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创建应用，查看hpa

kubectl apply -f /root/k8s-prom-hpa/podinfo/.

查看hpa

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过几分钟可以看见应用在弹性扩容

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https://zhuanlan.zhihu.com/p/34555654
https://github.com/kubernetes/community/blob/master/contributors/design-proposals/autoscaling/horizontal-pod-autoscaler.md#autoscaling-algorithm
https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/#support-for-metrics-APIs

要使用hpa，第一步是安装heapster（下面的10.135.19.77换成自己k8s可以访问到的ip）

wget https://github.com/kubernetes/heapster/archive/master.zip  
unzip master.zip  
cd heapster-master/

sed -i "s/gcr.io/google_containers/heapster-grafana-amd64:v4.4.3/index.tenxcloud.com/jimmy/heapster-grafana-amd64:v4.0.2/g" deploy/kube-config/influxdb/grafana.yaml

sed -i "s/gcr.io/google_containers/heapster-amd64:v1.4.0/index.tenxcloud.com/jimmy/heapster-amd64:v1.3.0-beta.1/g" deploy/kube-config/influxdb/heapster.yaml

sed -i "s/https://kubernetes.default/http://10.135.19.77:8080?inClusterConfig=false&useServiceAccount=false/g" deploy/kube-config/influxdb/heapster.yaml

sed -i "s/monitoring-influxdb.kube-system.svc/10.135.19.77/g" deploy/kube-config/influxdb/heapster.yaml

sed -i "s/gcr.io/google_containers/heapster-influxdb-amd64:v1.3.3/index.tenxcloud.com/jimmy/heapster-influxdb-amd64:v1.1.1/g" deploy/kube-config/influxdb/influxdb.yaml


/bin/bash deploy/kube.sh start

编写测试例子

编写文件a.yaml

apiVersion: extensions/v1beta1  
kind: Deployment  
metadata:  
  name: my-app
spec:  
  replicas: 2
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: registry.alauda.cn/yubang/paas_base_test
        ports:
        - containerPort: 80
        command: ["/bin/bash", "/var/start.sh"] 
        resources:  
          limits:  
            cpu: 0.01  
            memory: 64Mi

编写文件b.yaml

apiVersion: v1  
kind: Service  
metadata:  
  name: my-app-svc
  labels:
    app: my-app
spec:  
  ports:
  - port: 80
    targetPort: 80
    nodePort: 30964
  type: NodePort
  selector:
    app: my-app

编写文件c.yaml

apiVersion: autoscaling/v1  
kind: HorizontalPodAutoscaler  
metadata:  
  name: my-app-hpa
  namespace: default
spec:  
  scaleTargetRef:
    apiVersion: extensions/v1beta1
    kind: Deployment
    name: my-app
  minReplicas: 1
  maxReplicas: 10
  targetCPUUtilizationPercentage: 5

启动服务

kubectl apply -f a.yaml --validate  
kubectl apply -f b.yaml --validate  
kubectl apply -f c.yaml --validate

查看是否正常运行

kubectl get horizontalpodautoscaler

标签：pod

kubernetes pod的弹性伸缩

概念

实现hpa的两大关键

配置HPA

kubeadm方式

Rancher2.0

深入玩转K8S之利用Label控制Pod位置

k8s基于hpa实现pod弹性扩容