Vertical Pod Autoscaler

Overview

VPA collects and analyzes resource metrics for each container, adjusts the requested resources based on actual usage, and maintains the ratio of resource limit to request before and after the adjustment. VPA can increase or decrease CPU and memory resources as needed.

The rules are as follows:

• VPA generates the CPU and memory resource recommendations using the data collected by the Metrics API.
• VPA, in theory, recommends a minimum of 250 MiB of memory for each pod and 250 MiB divided by the number of containers in the pod for each container. It also recommends a minimum of 25m vCPUs for each pod and 25m divided by the number of containers in the pod for each container.

  When setting up a VPA, you can establish the minimum and maximum number of elastic resources in containers by configuring the containerPolicies field.

• If a container has both resource request and limit configured, VPA will provide resource recommendations. It will adjust the requested resources of the container to match the recommendations and generate recommended resource limit based on the ratio of the original resource request to the limit set during the container's initial creation.

  Assume that the requested vCPUs of a container are 100m and the limit is 200m (with a ratio of 1:2). If VPA recommends a requested vCPU of 80m, the container's vCPU limit will be 160m.

• VPA ensures its recommendations align with other resource limits. If the VPA recommendations conflict with a resource limit, they will not be adjusted to fit the limit. This means that the resource configuration suggested by VPA may go beyond other resource limits.

  Assume that the requested memory of a namespace cannot exceed 2 GiB. If VPA recommends a high memory configuration for a pod in that namespace, the total memory requested by the namespace may exceed 2 GiB after the pod's resource configuration is updated. This means the pod will not be scheduled.

Prerequisites

• The cluster version must be 1.25 or later.
• An add-on that provides Metrics API has been installed in the cluster. You can select one of the following add-ons based on your service requirements:
  • Kubernetes Metrics Server: provides basic resource usage metrics, such as container CPU and memory usage.
  • Cloud Native Cluster Monitoring: provides basic resource usage metrics using Prometheus. You need to register Prometheus as a service of Metrics API. For details, see Providing Resource Metrics Through the Metrics API.

Installing the Add-on

1. Log in to the CCE console and click the cluster name to access the cluster console. In the navigation pane, choose Add-ons, locate Vertical Pod Autoscaler on the right, and click Install.
2. On the Install Add-on page, configure the specifications as needed.
   • If you selected Preset, you can choose between Small, Medium, or Large based on the number of pods in the cluster. The system will automatically set the number of add-on pods and resource quotas according to the preset specifications. You can see the configurations on the console.
   • If you selected Custom, you can adjust the number of pods and resource quotas as needed. High availability is not possible with a single pod. If an error occurs on the node where the add-on instance runs, the add-on will fail.

3. Configure deployment policies for the add-on pods.
   

   • Scheduling policies do not take effect on add-on instances of the DaemonSet type.
   • When configuring multi-AZ deployment or node affinity, ensure that there are nodes meeting the scheduling policy and that resources are sufficient in the cluster. Otherwise, the add-on cannot run.

   Table 1 Configurations for add-on scheduling

   Parameter	Description
   Multi AZ	Preferred: Deployment pods of the add-on will be preferentially scheduled to nodes in different AZs. If all the nodes in the cluster are deployed in the same AZ, the pods will be scheduled to different nodes in that AZ. Required: Deployment pods of the add-on are forcibly scheduled to nodes in different AZs. There can be at most one pod in each AZ. If nodes in a cluster are not in different AZs, some add-on pods cannot run properly. If a node is faulty, add-on pods on it may fail to be migrated.
   Node Affinity	Not configured: Node affinity is disabled for the add-on. Node Affinity: Specify the nodes where the add-on is deployed. If you do not specify the nodes, the add-on will be randomly scheduled based on the default cluster scheduling policy. Specified Node Pool Scheduling: Specify the node pool where the add-on is deployed. If you do not specify the node pool, the add-on will be randomly scheduled based on the default cluster scheduling policy. Custom Policies: Enter the labels of the nodes where the add-on is to be deployed for more flexible scheduling policies. If you do not specify node labels, the add-on will be randomly scheduled based on the default cluster scheduling policy. If multiple custom affinity policies are configured, ensure that there are nodes that meet all the affinity policies in the cluster. Otherwise, the add-on cannot run.
   Toleration	Using both taints and tolerations allows (not forcibly) the add-on Deployment to be scheduled to a node with the matching taints, and controls the Deployment eviction policies after the node where the Deployment is located is tainted. The add-on adds the default tolerance policy for the node.kubernetes.io/not-ready and node.kubernetes.io/unreachable taints, respectively. The tolerance time window is 60s. For details, see Configuring Tolerance Policies.

4. Click Install.

Components

Change History