Merge pull request #38650 from mburke5678/GH#37505

GH#37505: Adding control plane to ClusterAutoscaler resource definition

Author: mburke5678

modules/cluster-autoscaler-about.adoc

@@ -8,7 +8,9 @@
 
 The cluster autoscaler adjusts the size of an {product-title} cluster to meet its current deployment needs. It uses declarative, Kubernetes-style arguments to provide infrastructure management that does not rely on objects of a specific cloud provider. The cluster autoscaler has a cluster scope, and is not associated with a particular namespace.
 
-The cluster autoscaler increases the size of the cluster when there are pods that failed to schedule on any of the current nodes due to insufficient resources or when another node is necessary to meet deployment needs. The cluster autoscaler does not increase the cluster resources beyond the limits that you specify.
+The cluster autoscaler increases the size of the cluster when there are pods that fail to schedule on any of the current worker nodes due to insufficient resources or when another node is necessary to meet deployment needs. The cluster autoscaler does not increase the cluster resources beyond the limits that you specify.
+
+The cluster autoscaler computes the total memory, CPU, and GPU on all nodes the cluster, even though it does not manage the control plane nodes. These values are not single-machine oriented. They are an aggregation of all the resources in the entire cluster. For example, if you set the maximum memory resource limit, the cluster autoscaler includes all the nodes in the cluster when calculating the current memory usage. That calculation is then used to determine if the cluster autoscaler has the capacity to add more worker resources.
 
 [IMPORTANT]
 ====
@@ -27,6 +29,8 @@ If the following types of pods are present on a node, the cluster autoscaler wil
 * Pods that cannot be moved elsewhere because of a lack of resources, incompatible node selectors or affinity, matching anti-affinity, and so on.
 * Unless they also have a `"cluster-autoscaler.kubernetes.io/safe-to-evict": "true"` annotation, pods that have a `"cluster-autoscaler.kubernetes.io/safe-to-evict": "false"` annotation.
 
+For example, you set the maximum CPU limit to 64 cores and configure the cluster autoscaler to only create machines that have 8 cores each. If your cluster starts with 30 cores, the cluster autoscaler can add up to 4 more nodes with 32 cores, for a total of 62.
+
 If you configure the cluster autoscaler, additional usage restrictions apply:
 
 * Do not modify the nodes that are in autoscaled node groups directly. All nodes within the same node group have the same capacity and labels and run the same system pods.
@@ -35,10 +39,8 @@ If you configure the cluster autoscaler, additional usage restrictions apply:
 * Confirm that your cloud provider quota is large enough to support the maximum node pools that you configure.
 * Do not run additional node group autoscalers, especially the ones offered by your cloud provider.
 
-
 The horizontal pod autoscaler (HPA) and the cluster autoscaler modify cluster resources in different ways. The HPA changes the deployment's or replica set's number of replicas based on the current CPU load. If the load increases, the HPA creates new replicas, regardless of the amount of resources available to the cluster. If there are not enough resources, the cluster autoscaler adds resources so that the HPA-created pods can run. If the load decreases, the HPA stops some replicas. If this action causes some nodes to be underutilized or completely empty, the cluster autoscaler deletes the unnecessary nodes.
 
-
 The cluster autoscaler takes pod priorities into account. The Pod Priority and Preemption feature enables scheduling pods based on priorities if the cluster does not have enough resources, but the cluster autoscaler ensures that the cluster has resources to run all pods. To honor the intention of both features, the cluster autoscaler includes a priority cutoff function. You can use this cutoff to schedule "best-effort" pods, which do not cause the cluster autoscaler to increase resources but instead run only when spare resources are available.
 
 Pods with priority lower than the cutoff value do not cause the cluster to scale up or prevent the cluster from scaling down. No new nodes are added to run the pods, and nodes running these pods might be deleted to free resources.

modules/cluster-autoscaler-cr.adoc

@@ -58,4 +58,6 @@ spec:
 [NOTE]
 ====
 When performing a scaling operation, the cluster autoscaler remains within the ranges set in the `ClusterAutoscaler` resource definition, such as the minimum and maximum number of cores to deploy or the amount of memory in the cluster. However, the cluster autoscaler does not correct the current values in your cluster to be within those ranges.
+
+The minimum and maximum CPUs, memory, and GPU values are determined by calculating those resources on all nodes in the cluster, even if the cluster autoscaler does not manage the nodes. For example, the control plane nodes are considered in the total memory in the cluster, even though the cluster autoscaler does not manage the control plane nodes.  
 ====

modules/installing-rhv-insecure-mode.adoc

@@ -24,12 +24,15 @@ Installing in *insecure* mode is not recommended, because it enables a potential
 +
 [source,terminal]
 ----
-ovirt_url: \https://ovirt.example.com/ovirt-engine/api
-ovirt_fqdn: ovirt.example.com
+ovirt_url: https://ovirt.example.com/ovirt-engine/api <1>
+ovirt_fqdn: ovirt.example.com <2>
 ovirt_pem_url: ""
-ovirt_username: ocpadmin@internal
-ovirt_password: super-secret-password
+ovirt_username: admin@internal
+ovirt_password: super-secret-password <3>
 ovirt_insecure: true
 ----
+<1> Specify the hostname or address of your oVirt engine.
+<2> Specify the fully qualified domain name of your oVirt engine.
+<3> Specify the the admin password for your oVirt engine.
 
 . Run the installer.