为什么单个节点群集只能使用一小部分的cpu配额? [英] Why does a single node cluster only have a small percentage of the cpu quota available?

问题描述

在上述问题中，我遇到了一个开始使用3个容器进行部署的问题.

In the above question, I had an issue starting a deployment with 3 containers.

进一步调查发现，似乎只有27％的CPU配额可用-这似乎很低.其余的CPU似乎已分配给某些默认的捆绑容器.

Upon further investigation, it appears there is only 27% of the CPU quota available - which seems very low. The rest of the CPU seems to be assigned to some default bundled containers.

通常如何缓解?是否需要更大的节点?是否需要手动设置限制?所有这些额外的容器是否必要?

How is this normally mitigated? Is a larger node required? Do limits need to be set manually? Are all those additional containers necessary?

推荐答案

单节点群集的1 cpu可能太小.

1 cpu for a single node cluster is probably too small.

从原始答案中的容器中，仪表板和流利条都可以删除:

From the containers in the original answer, both the dashboard and fluentd can be removed:

• 仪表板只是一个Web UI，如果您使用kubectl(IMO，您应该使用它)，该UI可能会消失；
• 流利的应该正在读取磁盘上的日志文件以将它们传送到某个地方(我认为GCP的日志聚合).

• the dashboard is just a web UI, which can go away if you use kubectl (which you should, IMO);
• fluentd should be reading the log files on disk to ship them somewhere (GCP's log aggregation, I think).

不必要的容器应该绑定到Deployment或ReplicaSet，这两个容器可以分别用kubectl get deployment和kubectl get rs列出.然后，您可以kubectl delete它们.

The unnecessary containers should be tied to a Deployment or ReplicaSet, which can be listed with kubectl get deployment and kubectl get rs, respectively. You can then kubectl delete them.

增加节点上的资源不应更改对基本Pod的要求，这意味着它们应该都是自由调度的.

Increasing the resources on the node should not change the requirements for the basic pods, meaning they should all be free scheduling.

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