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.
Did you find the above information helpful?
Please give us your feedback.
Thank you for your feedback.