tencent cloud


Cluster Scaling

Last updated: 2021-04-13 16:12:35


    This document describes how to manually or automatically scale a cluster to meet the resource requirements of the applications. You can scale a cluster in one of the following ways:


    1. You have logged into the TKE console.
    2. You have created a cluster.


    Manually adding/removing a node

    To scale out a cluster, you can manually add a node by creating a node or adding an existing node. To scale in a cluster, you can remove a node.

    Creating a node

    When creating a node, you can configure a new CVM on the Create Node page for cluster scale-out.
    For details, see Adding a Node.

    Adding an existing node


    • Currently, you can only add CVMs in the same VPC.
    • If you choose to add an existing node to the cluster, the operating system of the CVM will be reinstalled according to you settings.
    • If you choose to add an existing node to the cluster, the project of the CVM will be migrated to the project set for the cluster.
    • When adding a node with only one data disk to the cluster, you can choose to set the related parameters of data disk mounting.

    When adding a node, you can select and configure the CVM you want to add to the cluster on the Add Existing Node page.
    For details, see Adding a Node > Adding an Existing Node.

    Removing a node

    For directions on how to scale in a cluster, see Removing a Node.

    Automatically adding/removing a node via auto scaling

    Auto scaling relies on the community component Cluster Autoscaler (CA), which can dynamically adjust the number of nodes in a cluster to meet your resource requirements. For details on auto scaling, see Node Pool Overview.

    Scaling out via virtual node

    Virtual node is a kind of scheduling capability. It supports scheduling the Pods in a standard Kubernetes cluster to a virtual node that does not occupy the cluster server resource to implement dynamic scaling out when resources are insufficient.


    For issues related to scaling, see Auto-scaling Related.

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