Last updated：2021-05-11 10:41:31
Some customers prefer to configure small-capacity nodes when they create a Kubernetes cluster. Small-capacity nodes have the following disadvantages:
Large-capacity nodes have the following advantages:
The capacity of a master node is subject to the cluster scale. A large cluster requires large-capacity master nodes. The following table describes the recommended master node configurations in different cluster scales.
|Cluster scale (number of nodes)||Capacity of a master node|
|0 to 100||4 CPU cores, 8 GB memory, and 50 GB SSD data disk or higher|
|100 to 300||8 CPU cores, 16 GB memory, and 50 GB SSD data disk or higher|
|300 to 500||16 CPU cores, 32 GB memory, and 100 GB SSD data disk or higher|
|500 to 1,000||32 CPU cores, 64 GB memory, and 100 GB SSD data disk or higher|
|> 1,000||Contact Kingsoft Cloud.|
To ensure stability of nodes, KCE reserves some resources on the nodes based on the node specifications for Kubernetes components, such as kubelet, kube-proxy, and docker. For more information, see Reserve resources in a cluster. We recommend that you select node configurations based on the reserved resources and your business requirements.
Determine the CPU-memory ratio based on your business type.
If you need to run applications that require much memory, for example, Java applications, we recommend that you select KEC instances with a CPU-memory ratio of 1:8. If your applications are CPU intensive, you can select KEC instances with a CPU-memory ratio of 1:2. If you deploy different business applications together, we recommend that you attach labels to nodes of different instance types or configurations and schedule pods based on nodeAffinity.
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