Considerations for large clusters [page]deterministic
A cluster is a set of [nodes](#gloss:node) (physical or virtual machines) running Kubernetes agents, managed by the [control plane](#gloss:control-plane). Kubernetes supports clusters with up to 5,000 nodes. More specifically, Kubernetes is designed to accommodate configurations that meet *all* of the following criteria:
* No more than 110 pods per node * No more than 5,000 nodes * No more than 150,000 total pods * No more than 300,000 total containers
You can scale your cluster by adding or removing nodes. The way you do this depends on how your cluster is deployed.
## Cloud provider resource quotas {#quota-issues}
To avoid running into cloud provider quota issues, when creating a cluster with many nodes, consider: * Requesting a quota increase for cloud resources such as: * Computer instances * CPUs * Storage volumes * In-use IP addresses * Packet filtering rule sets * Number of load balancers * Network subnets * Log streams * Gating the cluster scaling actions to bring up new nodes in batches, with a pause between batches, because some cloud providers rate limit the creation of new instances.
## Control plane components
For a large cluster, you need a control plane with sufficient compute and other resources.
Typically you would run one or two control plane instances per failure zone, scaling those instances vertically first and then scaling horizontally after reaching the point of falling returns to (vertical) scale.
You should run at least one instance per failure zone to provide fault-tolerance. Kubernetes nodes do not automatically steer traffic towards control-plane endpoints that are in the same failure zone; however, your cloud provider might have its own mechanisms to do this.
For example, using a managed load balancer, you configure the load balancer to send traffic that originates from the kubelet and Pods in failure zone _A_, and direct that traffic only to the control plane hosts that are also in zone _A_. If a single control-plane host or endpoint failure zone _A_ goes offline, that means that all the control-plane traffic for nodes in zone _A_ is now being sent between zones. Running multiple control plane hosts in each zone makes that outcome less likely.
### etcd storage
To improve performance of large clusters, you can store Event objects in a separate dedicated etcd instance.
When creating a cluster, you can (using custom tooling):
* start and configure additional etcd instance * configure the [API server](#gloss:kube-apiserver) to use it for storing events
See [Operating etcd clusters for Kubernetes](/docs/tasks/administer-cluster/configure-upgrade-etcd/) and [Set up a High Availability etcd cluster with kubeadm](/docs/setup/production-environment/tools/kubeadm/setup-ha-etcd-with-kubeadm/) for details on configuring and managing etcd for a large cluster.
## Addon resources
Kubernetes [resource limits](/docs/concepts/configuration/manage-resources-containers/) help to minimize the impact of memory leaks and other ways that pods and containers can impact on other components. These resource limits apply to [addon](#gloss:addons) resources just as they apply to application workloads.
For example, you can set CPU and memory limits for a logging component:
```yaml ... containers: - name: fluentd-cloud-logging image: fluent/fluentd-kubernetes-daemonset:v1 resources: limits: cpu: 100m memory: 200Mi ```
Addons' default limits are typically based on data collected from experience running each addon on small or medium Kubernetes clusters. When running on large clusters, addons often consume more of some resources than their default limits. If a large cluster is deployed without adjusting these values, the addon(s) may continuously get killed because they keep hitting the memory limit. Alternatively, the addon may run but with poor performance due to CPU time slice restrictions.
To avoid running into cluster addon resource issu …(trimmed)