- Function Overview
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User Guide
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API Reference
- Before You Start
- API Overview
- Calling APIs
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APIs
- Autopilot Cluster Management
- Add-on Management for Autopilot Clusters
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Autopilot Cluster Upgrade
- Upgrading a Cluster
- Obtaining Cluster Upgrade Task Details
- Retrying a Cluster Upgrade Task
- Obtaining a List of Cluster Upgrade Task Details
- Performing a Pre-upgrade Check for a Cluster
- Obtaining Details About a Pre-upgrade Check Task of a Cluster
- Obtaining a List of Pre-upgrade Check Tasks of a Cluster
- Performing a Post-upgrade Check for a Cluster
- Backing Up a Cluster
- Obtaining a List of Cluster Backup Task Details
- Obtaining the Cluster Upgrade Information
- Obtaining a Cluster Upgrade Path
- Obtaining the Configuration of Cluster Upgrade Feature Gates
- Enabling the Cluster Upgrade Process Booting Task
- Obtaining a List of Upgrade Workflows
- Obtaining Details About a Specified Cluster Upgrade Booting Task
- Updating the Status of a Specified Cluster Upgrade Booting Task
- Quota Management for Autopilot Clusters
- Tag Management for Autopilot Clusters
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Chart Management for Autopilot Clusters
- Uploading a Chart
- Obtaining a Chart List
- Obtaining a Release List
- Creating a Release
- Updating a Chart
- Deleting a Chart
- Updating a Release
- Obtaining a Chart
- Deleting a Release
- Obtaining a Release
- Downloading a Chart
- Obtaining Chart Values
- Obtaining Historical Records of a Release
- Obtaining the Quota of a User Chart
- Kubernetes APIs
- Permissions and Supported Actions
- Appendix
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FAQs
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Storage
- Can PVs of the EVS Type in a CCE Autopilot Cluster Be Restored After They Are Deleted or Expire?
- What Can I Do If a Storage Volume Fails to Be Created?
- Can CCE Autopilot PVCs Detect Underlying Storage Faults?
- How Can I Delete the Underlying Storage If It Remains After a Dynamically Created PVC is Deleted?
- Permissions
- General Reference
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HPA Policies
Horizontal Pod Autoscaling (HPA) in Kubernetes implements horizontal scaling of pods. In a CCE HPA policy, you can configure different cooldown time windows and scaling thresholds for different applications based on the Kubernetes HPA.
Prerequisites
- Kubernetes Metrics Server: provides basic resource usage metrics, such as container CPU and memory usage.
- Creating an HPA Policy Using Custom Metrics: Custom metrics need to be aggregated to the Kubernetes API server for auto scaling.
Creating an HPA Policy
- Log in to the CCE console and click the cluster name to access the cluster console.
- In the navigation pane on the left, choose Policies. On the Scaling Policies, click the HPA Policies tab and then Create HPA Policy in the upper right corner.
- Configure basic information.
- Policy Name: Enter a name for the policy.
- Namespace: Select the namespace that the workload belongs to.
- Associated Workload: Select the workload that the policy is configured for.
- Configure other parameters.
Table 1 HPA policy parameters Parameter
Description
Pod Range
Minimum and maximum numbers of pods.
When a policy is triggered, the pods are scaled within this range.
NOTICE:In CCE Autopilot clusters, if you use a dedicated load balancer for a workload, the number of pods cannot exceed the load balancer's backend server group quota, which is 500 by default. If this limit is exceeded, no more pods can be added as the load balancer backend.
Scaling Behavior
- Default: Scale workloads using the Kubernetes default behavior. For details, see Default Behavior.
- Custom: Scale workloads using custom policies such as stabilization window, steps, and priorities. Unspecified parameters use the values recommended by Kubernetes.
- Disable scale-out/scale-in: Select whether to disable scale-out or scale-in.
- Stabilization Window: a period during which CCE continuously checks whether the metrics used for scaling keep fluctuating. CCE triggers scaling if the desired state is not maintained for the entire window. This window restricts the unwanted flapping of pod count due to metric changes.
- Step: specifies the scaling step. You can set the number or percentage of pods to be scaled in or out within a specified period. If there are multiple policies, you can select the policy that maximizes or minimizes the number of pods.
System Policy
- Metric: You can select CPU usage or Memory usage.
NOTE:
Usage = CPUs or memory used by pods/Requested CPUs or memory
- Desired Value: Enter the desired average resource usage.
This parameter indicates the desired value of the selected metric. Number of pods to be scaled (rounded up) = (Current metric value/Desired value) x Number of current pods
NOTE:
When calculating the number of pods to be added or reduced, the HPA policy uses the maximum number of pods in the last 5 minutes.
- Tolerance Range: Scaling is not triggered when the metric value is within the tolerance range. The desired value must be within the tolerance range.
If the metric value is greater than the scale-in threshold and less than the scale-out threshold, no scaling is triggered. This parameter is only supported in clusters v1.15 or later.
- Click Create.
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