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当 pod 的负载增加时，Kubernetes HPA 可以正常工作，但负载降低后，部署规模不会改变。这是我的 HPA 文件：

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: baseinformationmanagement
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: baseinformationmanagement
  minReplicas: 1
  maxReplicas: 3
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 80
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

我的 Kubernetes 版本：

> kubectl version
Client Version: version.Info{Major:"1", Minor:"16", GitVersion:"v1.16.1", GitCommit:"d647ddbd755faf07169599a625faf302ffc34458", GitTreeState:"clean", BuildDate:"2019-10-02T17:01:15Z", GoVersion:"go1.12.10", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"17", GitVersion:"v1.17.2", GitCommit:"59603c6e503c87169aea6106f57b9f242f64df89", GitTreeState:"clean", BuildDate:"2020-01-18T23:22:30Z", GoVersion:"go1.13.5", Compiler:"gc", Platform:"linux/amd64"}

这是我的 HPA 描述：

> kubectl describe hpa baseinformationmanagement
Name:                                                     baseinformationmanagement
Namespace:                                                default
Labels:                                                   <none>
Annotations:                                              kubectl.kubernetes.io/last-applied-configuration:
                                                            {"apiVersion":"autoscaling/v2beta2","kind":"HorizontalPodAutoscaler","metadata":{"annotations":{},"name":"baseinformationmanagement","name...
CreationTimestamp:                                        Sun, 27 Sep 2020 06:09:07 +0000
Reference:                                                Deployment/baseinformationmanagement
Metrics:                                                  ( current / target )
  resource memory on pods  (as a percentage of request):  49% (1337899008) / 70%
  resource cpu on pods  (as a percentage of request):     2% (13m) / 50%
Min replicas:                                             1
Max replicas:                                             3
Deployment pods:                                          2 current / 2 desired
Conditions:
  Type            Status  Reason              Message
  ----            ------  ------              -------
  AbleToScale     True    ReadyForNewScale    recommended size matches current size
  ScalingActive   True    ValidMetricFound    the HPA was able to successfully calculate a replica count from memory resource utilization (percentage of request)
  ScalingLimited  False   DesiredWithinRange  the desired count is within the acceptable range
Events:           <none>
4

1 回答 1

6

您的 HPA 指定了内存和 CPU 目标。Horizo ​​ntal Pod Autoscaler文档说明：

如果在 Horizo​​ntalPodAutoscaler 中指定了多个指标，则会针对每个指标进行此计算，然后选择所需副本数中的最大值。

实际副本目标是当前副本计数以及当前和目标利用率的函数（相同链接）：

desiredReplicas = ceil[currentReplicas * ( currentMetricValue / desiredMetricValue )]

特别是对于内存：currentReplicas是2；currentMetricValue是 49；desiredMetricValue是 80。所以目标副本数是

desiredReplicas = ceil[       2        * (         49        /         80         )]
desiredReplicas = ceil[       2        *                   0.61                    ]
desiredReplicas = ceil[                          1.26                              ]
desiredReplicas = 2

即使您的服务完全空闲，这也会导致（至少）2 个副本，除非服务选择将内存释放回操作系统；这通常取决于语言运行时并且有点超出您的控制。

仅删除内存目标和仅基于 CPU 的自动缩放可能会更符合您的预期。

于 2020-10-03T14:34:35.830 回答