混合云K8s容器化应用弹性伸缩实战

Posted 2021-05-14 阿里云开发者

简介：混合云K8s容器化应用弹性伸缩实战

1. 前提条件

本最佳实践的软件环境要求如下：
应用环境：
①容器服务ACK基于专有云V3.10.0版本。
②公共云云企业网服务CEN。
③公共云弹性伸缩组服务ESS。
配置条件：
1）使用专有云的容器服务或者在ECS上手动部署敏捷PaaS。
2）开通云专线，打通容器服务所在VPC与公共云上的VPC。
3）开通公共云弹性伸缩组服务（ESS）。

2. 背景信息

本实践基于K8s的业务集群运行在专有云上，对测试业务进行压力测试，主要基于以下三种产品和能力：
①利用阿里云的云企业网专线打通专有云和公共云，实现两朵云上VPC网络互通。
②利用K8s（Kubernetes）的HPA能力，实现容器的水平伸缩。
③利用K8s的Cluster Autoscaler和阿里云弹性伸缩组ESS能力实现节点的自动伸缩。

HPA（Horizontal Pod Autoscaler）是K8s的一种资源对象，能够根据CPU、内存等指标对statefulset、deployment等对象中的pod数量进行动态伸缩，使运行在上面的服务对指标的变化有一定的自适应能力。

当被测试业务指标达到上限时，触发HPA自动扩容业务pod；当业务集群无法承载更多pod时，触发公共云的ESS服务，在公共云内扩容出ECS并自动添加到专有云的K8s集群。

图 1：架构原理图

3. 配置HPA

本示例创建了一个支持HPA的nginx应用，创建成功后，当Pod的利用率超过本例中设置的20%利用率时，则会进行水平扩容，低于20%的时候会进行缩容。

1.若使用自建K8s集群，则通过yaml文件配置HPA

1）创建一个nginx应用，必须为应用设置request值，否则HPA不会生效。

apiVersion:
app/v1beta2
kind: Deployment
spec:
  template:
    metadata:
      creationTimestamp: null
      labels:
        app: hpa-test
    spec:
        dnsPolicy: ClusterFirst     
        terminationGracePeriodSeconds:30         
        containers:
        image: '192.168.**.***:5000/admin/hpa-example:v1'
        imagePullPolicy: IfNotPresent
        terminationMessagePolicy:File
        terminationMessagePath:/dev/termination-log
        name: hpa-test
        resources:
          requests:
            cpu: //必须设置request值
        securityContext: {}
        restartPolicy:Always
        schedulerName:default-scheduler
  replicas: 1
  selector: 
    matchLabels:
        app: hpa-test
  revisionHistoryLimit: 10
  strategy: 
    type: RollingUpdate
    rollingUpdate:
        maxSurge: 25%
        maxUnavailable: 25%
     progressDeadlineSeconds: 600

2）创建HPA。

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
 annotations:
    autoscaling.alpha.kubernetes.io/conditions:'[{"type":"AbleToScale","status":"True","lastTransitionTime":"2020-04-29T06:57:28Z","reason":"ScaleDownStabilized","message":"recent
    recommendations were higher than current one, applying the highest recent
    recommendation"},{"type":"ScalingActive","status":"True","lastTransitionTime":"2020-04-29T06:57:28Z","reason":"ValidMetricFound","message":"theHPA
    was able to successfully calculate a replica count from cpu resource
    utilization(percentage of
    request)"},{"type":"ScalingLimited","status":"False","lastTransitionTime":"2020-04-29T06:57:28Z","reason":"DesiredWithinRange","message":"thedesired
    count is within the acceptable range"}]'
    autoscaling.alpha.kubernetes.io/currentmetrics:'[{"type":"Resource","resource":{"name":"cpu","currentAverageUtilization":0,"currentAverageValue":"0"}}]'

creationTimestamp: 2020-04-29T06:57:13Z
name: hpa-test
namespace: default
resourceVersion: "3092268"
selfLink:
/apis/autoscaling/v1/namespaces/default/horizontalpodautoscalers/hpa01
uid: a770ca26-89e6-11ea-a7d7-00163e0106e9
spec:
    maxReplicas: //设置pod数量 
    minReplicas: 1
    scaleTargetRef:
       apiVersion: apps/v1beta2
       kind: Deployment
       name: centos 
            targetCPUUtilizationPercentage://设置CPU阈值

2.若使用阿里云容器服务，需要在部署应用时选择配置HPA

图2：访问设置

4. 配置Cluster Autoscaler

资源请求（Request）的正确、合理设置，是弹性伸缩的前提条件。节点自动伸缩组件基于K8s资源调度的分配情况进行伸缩判断，节点中资源的分配通过资源请（Request）进行计算。

当Pod由于资源请求（Request）无法满足并进入等待（Pending）状态时，节点自动伸缩组件会根据弹性伸缩组配置信息中的资源规格以及约束配置，计算所需的节点数目。

如果可以满足伸缩条件，则会触发伸缩组的节点加入。而当一个节点在弹性伸缩组中且节点上Pod的资源请求低于阈值时，节点自动伸缩组件会将节点进行缩容。

1.配置弹性伸缩组ESS

1）创建ESS弹性伸缩组，记录最小实例数和最大实例数。

图3：修改伸缩组

2）创建伸缩配置，记录伸缩配置的id。

图4：伸缩配置

#!/bin/sh
yum install -y ntpdate && ntpdate -u ntp1.aliyun.com && curl http:// example.com/public/hybrid/attach_local_node_aliyun.sh | bash -s -- --docker-version 17.06.2-ce-3 --token
9s92co.y2gkocbumal4fz1z --endpoint 192.168.**.***:6443 --cluster-dns 10.254.**.**
--region cn-huhehaote
echo "{" > /etc/docker/daemon.json
echo "\\"registry-mirrors\\": [" >>
/etc/docker/daemon.json
echo "\\"https://registry-vpc.cn-huhehaote.aliyuncs.com\\"" >> /etc/docker/daemon.json
echo "]," >> /etc/docker/daemon.json
echo "\\"insecure-registries\\": [\\"https://192.168.**.***:5000\\"]" >> /etc/docker/daemon.json
echo "}" >> /etc/docker/daemon.json
systemctl restart docker 

2.K8s集群部署autoscaler

kubectl apply -f ca.yml

参考ca.yml创建autoscaler，注意修改如下配置与实际环境相对应。

access-key-id: "TFRBSWlCSFJyeHd2QXZ6****"
access-key-secret: "bGIyQ3NuejFQOWM0WjFUNjR4WTVQZzVPRXND****"
region-id: "Y24taHVoZWhh****"

ca.yal代码如下：

---
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
  name: cluster-autoscaler
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
rules:
- apiGroups: [""]
  resources: ["events","endpoints"]
  verbs: ["create", "patch"]
- apiGroups: [""]
  resources: ["pods/eviction"]
  verbs: ["create"]
- apiGroups: [""]
  resources: ["pods/status"]
  verbs: ["update"]
- apiGroups: [""]
  resources: ["endpoints"]
  resourceNames: ["cluster-autoscaler"]
  verbs: ["get","update"]
- apiGroups: [""]
  resources: ["nodes"]
  verbs: ["watch","list","get","update"]
- apiGroups: [""]
  resources: ["pods","services","replicationcontrollers","persistentvolumeclaims","persistentvolumes"]
  verbs: ["watch","list","get"]
- apiGroups: ["extensions"]
  resources: ["replicasets","daemonsets"]
  verbs: ["watch","list","get"]
- apiGroups: ["policy"]
  resources: ["poddisruptionbudgets"]
  verbs: ["watch","list"]
- apiGroups: ["apps"]
  resources: ["statefulsets"]
  verbs: ["watch","list","get"]
- apiGroups: ["storage.k8s.io"]
  resources: ["storageclasses"]
  verbs: ["watch","list","get"]

---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: cluster-autoscaler
  namespace: kube-system
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
rules:
- apiGroups: [""]
  resources: ["configmaps"]
  verbs: ["create","list","watch"]
- apiGroups: [""]
  resources: ["configmaps"]
  resourceNames: ["cluster-autoscaler-status", "cluster-autoscaler-priority-expander"]
  verbs: ["delete","get","update","watch"]

---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: cluster-autoscaler
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-autoscaler
subjects:
  - kind: ServiceAccount
    name: cluster-autoscaler
    namespace: kube-system

---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: cluster-autoscaler
  namespace: kube-system
  labels:
    k8s-addon: cluster-autoscaler.addons.k8s.io
    k8s-app: cluster-autoscaler
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: cluster-autoscaler
subjects:
  - kind: ServiceAccount
    name: cluster-autoscaler
    namespace: kube-system
---
apiVersion: v1
kind: Secret
metadata:
  name: cloud-config
  namespace: kube-system
type: Opaque
data:
  access-key-id: "TFRBSWlCSFJyeHd2********"
  access-key-secret: "bGIyQ3NuejFQOWM0WjFUNjR4WTVQZzVP*********"
  region-id: "Y24taHVoZW********"

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cluster-autoscaler
  namespace: kube-system
  labels:
    app: cluster-autoscaler
spec:
  replicas: 1
  selector:
    matchLabels:
      app: cluster-autoscaler
  template:
    metadata:
      labels:
        app: cluster-autoscaler
    spec:
      dnsConfig:
        nameservers:
          - 100.XXX.XXX.XXX
          - 100.XXX.XXX.XXX
      nodeSelector:
        ca-key: ca-value
      priorityClassName: system-cluster-critical
      serviceAccountName: admin
      containers:
        - image: 192.XXX.XXX.XXX:XX/admin/autoscaler:v1.3.1-7369cf1
          name: cluster-autoscaler
          resources:
            limits:
              cpu: 100m
              memory: 300Mi
            requests:
              cpu: 100m
              memory: 300Mi
          command:
            - ./cluster-autoscaler
            - '--v=5'
            - '--stderrthreshold=info'
            - '--cloud-provider=alicloud'
            - '--scan-interval=30s'
            - '--scale-down-delay-after-add=8m'
            - '--scale-down-delay-after-failure=1m'
            - '--scale-down-unready-time=1m'
            - '--ok-total-unready-count=1000'
            - '--max-empty-bulk-delete=50'
            - '--expander=least-waste'
            - '--leader-elect=false'
            - '--scale-down-unneeded-time=8m'
            - '--scale-down-utilization-threshold=0.2'
            - '--scale-down-gpu-utilization-threshold=0.3'
            - '--skip-nodes-with-local-storage=false'
            - '--nodes=0:5:asg-hp3fbu2zeu9bg3clraqj'
          imagePullPolicy: "Always"
          env:
            - name: ACCESS_KEY_ID
              valueFrom:
                secretKeyRef:
                  name: cloud-config
                  key: access-key-id
            - name: ACCESS_KEY_SECRET
              valueFrom:
                secretKeyRef:
                  name: cloud-config
                  key: access-key-secret
            - name: REGION_ID
              valueFrom:
                secretKeyRef:
                  name: cloud-config
                  key: region-id

5. 执行结果

模拟业务访问：

启动busybox镜像，在pod内执行如下命令访问以上应用的service，可以同时启动多个pod增加业务负载。while true;do wget -q -O- http://hpa-test/index.html;done

观察HPA：

加压前

图 5：加压前

加压后
当CPU值达到阈值后，会触发pod的水平扩容。

图 6：加压后1

图 7：加压后2

观察Pod：

当集群资源不足时，新扩容出的pod处于pending状态，此时将触发cluster autoscaler，自动扩容节点。

图8：伸缩活动

我们是阿里云智能全球技术服务-SRE团队，我们致力成为一个以技术为基础、面向服务、保障业务系统高可用的工程师团队；提供专业、体系化的SRE服务，帮助广大客户更好地使用云、基于云构建更加稳定可靠的业务系统，提升业务稳定性。我们期望能够分享更多帮助企业客户上云、用好云，让客户云上业务运行更加稳定可靠的技术，您可用钉钉扫描下方二维码，加入阿里云SRE技术学院钉钉圈子，和更多云上人交流关于云平台的那些事。

原文链接：https://developer.aliyun.com/article/783989?

版权声明：本文内容由阿里云实名注册用户自发贡献，版权归原作者所有，阿里云开发者社区不拥有其著作权，亦不承担相应法律责任。具体规则请查看《阿里云开发者社区用户服务协议》和《阿里云开发者社区知识产权保护指引》。如果您发现本社区中有涉嫌抄袭的内容，填写侵权投诉表单进行举报，一经查实，本社区将立刻删除涉嫌侵权内容。