Kubernetes-自动扩展器HPA、VPA、CA
文章目录
- 一、Kubernetes自动扩展器
-
- 1.1、Kubernetes Pod水平自动伸缩（HPA）
-
- 1.1.1、HPA简介
- 1.1.2、HPA示例
- 1.2、Kubernetes Pod垂直自动伸缩（VPA）
-
- 1.2.1、VPA 简介
- 1.2.2、VPA示例
-
- 1.2.2.1、部署metrics-server
- 1.2.2.2、部署vertical-pod-autoscaler
- 1.2.2.3、updateMode: "Off"（此模式仅获取资源推荐不更新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）
- 1.2.2.5、VPA使用限制&优势
- 1.3、Kubernetes 集群自动缩放器（CA）
-
- 1.3.1、CA简介
- 1.4、Pod 自动缩放的前置时间
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;