Min replicas: 1 In the confirmation pop-up window, click Remove to remove the HPA. The HPA object increases the minimum to 5. You can specify a cool down period by configuring the stabilizationWindowSeconds field. you want to run, as well as the CPU utilization or memory utilization your pods should target. directly to the replica count of the deployment configuration. Memory: 45440Ki The HPA scales the pods associated with that object to maintain the CPU usage you specify. Scaling policies allow you to restrict the rate that HPAs scale pods up or down by setting a specific number or specific percentage to scale in a specified period of time. In the Topology view, click the node to reveal the side panel. If any of the values for CPU and memory usage are missing, a warning is displayed. For more information on To view the status conditions on a pod, use the following command with the name of the pod: The conditions appear in the Conditions field in the output. To use horizontal pod autoscalers, your cluster administrator must have For example, a stabilization window is specified for the scaleDown field: In the above example, all desired states for the past 5 minutes are considered. This approximates a rolling maximum, and avoids having the scaling algorithm frequently remove pods only to trigger recreating an equivalent pod just moments later. On average: An increase in replica count must lead to an overall decrease in memory Amount of memory used. during resource spikes, such as during start up. This allows for more stability during the HPA decision. Usage: The amount of scaling up by the percentage of pods. The HPA makes a scaling decision based on the observed CPU or memory utilization values of pods in an OKD cluster. When these metrics are available, the horizontal pod autoscaler computes configured, the output appears similar to the following, with Cpu and Memory displayed under Usage. In the Topology view, click the node to reveal the side pane. specify how OKD should automatically increase or decrease the scale of a replication controller or deployment configuration, based on metrics collected For deployment configurations, scaling corresponds The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain The controller then takes the average of utilization across all targeted pods and produces a ratio that is used to scale the number of desired replicas. You can manually scale up by adjusting the number of replicas in the replica set or deployment set, if needed. The autoscaling/v2 API allows you to add scaling policies to a horizontal pod autoscaler. During horizontal pod autoscaling, there might be a rapid scaling of events without a time gap. Pods without known metrics have 0% CPU usage when scaling up and 100% CPU when scaling down. When the number of pods falls below 40, the pods-based policy is applied, because the pod-based number is greater than the percent-based number. The default value is. You can also restrict the scaling by timed iterations. Metadata: The kubelet enforces the resource limit that you specify for a container to ensure that the container is not allowed to use more than the specified limit. A False condition generally indicates a problem with fetching metrics. A True condition indicates scaling is allowed. Max replicas: 4 To use horizontal pod autoscalers, your cluster administrator must have properly configured cluster metrics. In the pod specifications, you must specify the resource requests, such as CPU and memory. To scale for a percentage, create a HorizontalPodAutoscaler object similar to the following for an existing object: You can use the status conditions set to determine in any way. A scaling policy controls how the OKD horizontal pod autoscaler (HPA) scales pods. After you create a horizontal pod autoscaler, OKD begins to query the CPU and/or memory resource metrics on the pods. Kind: PodMetrics Use the OKD web console to check the memory behavior of your application In this example, when the number of pods is greater than 40, the percent-based policy is used for scaling down, as that policy results in a larger change, as required by the selectPolicy. The controller manager obtains the utilization metrics from the resource metrics API for per-pod resource metrics like CPU or memory, for each pod that is targeted by the HPA. The autoscaling algorithm uses this window to infer a previous desired state and avoid unwanted changes to workload scale. the specified memory utilization across all pods. The scheduler uses the resource request that you specify for containers in a pod, to decide which node to place the pod on. The HPA uses this specification to determine the resource utilization and then scales the target up or down. Utilization is the ratio between the current resource usage to the requested resource of the pod. Specify the name of the object to scale.
OKD automatically accounts for resources and prevents unnecessary autoscaling and ensure that your application meets these requirements before using For memory utilization, you can specify the minimum and maximum number of pods and the average memory utilization but can take one to two minutes before metrics become available. If not specified or negative, a default autoscaling policy is used. '{"ScaleUp":{"StabilizationWindowSeconds":0,"SelectPolicy":"Max","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":15},{"Type":"Percent","Value":100,"PeriodSeconds":15}]},\ You can create an HPA for any Deployment, DeploymentConfig, properly configured cluster metrics.
You can use the oc describe PodMetrics
Namespace: prom. The default value is. Usage: Can be used to calculate a percentage of the pods requested CPU. If metrics are To use this feature, you must configure The ScalingActive condition indicates whether the HPA is enabled (for example, the replica count of the target is not zero) and is able to calculate desired metrics. During this period, the controller manager queries the CPU, memory utilization, or both, against what is defined in the YAML file for the HPA. Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet object. Reference: ReplicationController/cm-test The HPA status conditions are available with the v2 version of the From the Actions drop-down list, select Add HorizontalPodAutoscaler to open the Add HorizontalPodAutoscaler form. Specify a name for this horizontal pod autoscaler object. The initial deployment requires 3 pods. of the replication controller. As a developer, you can use a horizontal pod autoscaler (HPA) to The HPA responds with the following status conditions: The AbleToScale condition indicates whether HPA is able to fetch and update metrics, as well as whether any backoff-related conditions could prevent scaling. whether or not the horizontal pod autoscaler (HPA) is able to scale and whether or not it is currently restricted A decrease in replica count must lead to an overall increase in per-pod memory To implement the HPA, all targeted pods must have a resource request set on their containers. The ScalingLimited condition indicates that the desired scale was capped by the maximum or minimum of the horizontal pod autoscaler. The default value for scaling up by percentage is 100%. Note that autoscaling ReplicationController pods: 1 current / 1 desired ReplicaSet, ReplicationController, or StatefulSet object. Determines the length of a scaling iteration. scales the pods associated with that object to maintain the average memory utilization you specify, either a direct value or a percentage Horizontal Pod Autoscaling of Quarkus Application Based on Memory Utilization. CreationTimestamp: Fri, 16 Jun 2017 18:09:22 +0000 The HPA reduces 4 pods at a time (type: Pods and value: 4), over 30 seconds (periodSeconds: 30), until there are 20 replicas remaining (minReplicas). configuration. The object must exist and be a. Optionally, specify the minimum number of replicas when scaling down. readiness checks to determine if a new pod is ready for use. Determines the amount of scaling, either the number of pods or percentage of pods, during each iteration. Creation Timestamp: 2019-05-23T18:47:56Z these objects, see Understanding Deployment and DeploymentConfig objects. Metrics: ( current / target ) Memory: 0 If there are 80 pod replicas, in the first iteration the HPA reduces the pods by 8, which is 10% of the 80 pods (based on the type: Percent and value: 10 parameters), over one minute (periodSeconds: 60). Namespace: openshift-kube-scheduler, API Version: metrics.k8s.io/v1beta1 For a DeploymentConfig, use apps.openshift.io/v1. Self Link: /apis/metrics.k8s.io/v1beta1/namespaces/openshift-kube-scheduler/pods/openshift-kube-scheduler-ip-10-0-135-131.ec2.internal memory-based autoscaling. applies only to the latest deployment in the Complete phase. The initial deployment requires 3 pods.
Specify, Determines the time period the HPA should look back at desired states. From the Actions drop-down list, select Remove HorizontalPodAutoscaler. If CPU usage on the pods reaches 75%, the pods increase to 7: The horizontal pod autoscaler (HPA) extends the concept of pod auto-scaling. The HPA increases and decreases the number of replicas between the minimum and maximum numbers to maintain the specified CPU utilization across all pods. A True condition indicates that you need to raise or lower the minimum or maximum replica count in order to scale. usage. Missing resource request values can affect the optimal performance of the HPA. autoscaling API. have 0 CPU usage when scaling up and the autoscaler ignores the pods when scaling down. Verify that the horizontal pod autoscaler was created: Using the OKD CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Containers: If a utilization value target is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each pod. The amount of scaling up by the number of pods. The object must exist. annotations: The HPA Using the OKD CLI, you can create a horizontal pod autoscaler (HPA) to automatically scale an existing Deployment, DeploymentConfig, ReplicaSet, ReplicationController, or StatefulSet object. Window: 1m0s, Name: cm-test From the Actions drop-down list, select Edit HorizontalPodAutoscaler to open the Edit Horizontal Pod Autoscaler form. Determines which policy to use first, if multiple policies are defined. from the pods that belong to that replication controller or deployment The following is an example of a pod that is unable to scale: The following is an example of a pod that could not obtain the needed metrics for scaling: The following is an example of a pod where the requested autoscaling was less than the required minimums: You can view the status conditions set on a pod by the horizontal pod autoscaler (HPA). A True condition indicates metrics is working properly. You can use the oc describe PodMetrics
(working set) usage per-pod. An HPA cannot be added to deployments that are part of an Operator-backed service, Knative service, or Helm chart.
"ScaleDown":{"StabilizationWindowSeconds":300,"SelectPolicy":"Min","Policies":[{"Type":"Pods","Value":4,"PeriodSeconds":60},{"Type":"Percent","Value":10,"PeriodSeconds":60}]}}', NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE
