kubernetes - 我的 kubernetes 集群没有缩小

Question

我有 kuberentes 集群。一位主人和一位 worker 。我安装了用于自动缩放的指标服务器，然后运行压力测试

$ kubectl run autoscale-test --image=ubuntu:16.04 --requests=cpu=1000m --command sleep 1800
deployment "autoscale-test" created
$ kubectl autoscale deployment autoscale-test --cpu-percent=25 --min=1 --max=5
deployment "autoscale-test" autoscaled
$ kubectl get hpa
NAME             REFERENCE                   TARGETS    MINPODS   MAXPODS   REPLICAS   AGE
autoscale-test   Deployment/autoscale-test   0% / 25%   1         5         1          1m
$ kubectl get pod
NAME                              READY     STATUS    RESTARTS   AGE
autoscale-test-59d66dcbf7-9fqr8   1/1       Running   0          9m
kubectl exec autoscale-test-59d66dcbf7-9fqr8 -- apt-get update
kubectl exec autoscale-test-59d66dcbf7-9fqr8 -- apt-get install stress

$ kubectl exec autoscale-test-59d66dcbf7-9fqr8 -- stress --cpu 2 --timeout 600s &
stress: info: [227] dispatching hogs: 2 cpu, 0 io, 0 vm, 0 hdd

一切正常，pod 已自动缩放，但之后由 autoscale 创建的 pod 仍在运行，并且在压力测试后它们不会终止hpa 显示 0% 的 cpu 正在使用，但 5 个自动缩放的 pod 仍在运行

#kubectl get hpa
NAME             REFERENCE                   TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
autoscale-test   Deployment/autoscale-test   0%/25%    1         5         5          74m

#kubectl get pods --all-namespaces
NAMESPACE     NAME                             READY   STATUS    RESTARTS   AGE
default       autoscale-test-8f4d84bbf-7ddjw   1/1     Running   0          61m
default       autoscale-test-8f4d84bbf-bmr59   1/1     Running   0          61m
default       autoscale-test-8f4d84bbf-cxt26   1/1     Running   0          61m
default       autoscale-test-8f4d84bbf-x9jws   1/1     Running   0          61m
default       autoscale-test-8f4d84bbf-zbhvk   1/1     Running   0          71m

我等了一个小时，但什么也没发生

Answer 1

来自文档:

--horizo​​ntal-pod-autoscaler-downscale-delay:此选项的值是一个持续时间，指定自动缩放器必须等待多长时间才能在当前操作之后执行另一个缩减操作已经完成。默认值为 5 分钟 (5m0s)。

Note: When tuning these parameter values, a cluster operator should be aware of the possible consequences. If the delay (cooldown) value is set too long, there could be complaints that the Horizontal Pod Autoscaler is not responsive to workload changes. However, if the delay value is set too short, the scale of the replicas set may keep thrashing as usual.

Finally, just before HPA scales the target, the scale recommendation is recorded. The controller considers all recommendations within a configurable window choosing the highest recommendation from within that window. This value can be configured using the --horizontal-pod-autoscaler-downscale-stabilization-window flag, which defaults to 5 minutes. This means that scaledowns will occur gradually, smoothing out the impact of rapidly fluctuating metric values.