Merge pull request #71778 from mburke5678/ocpbugs-28861-remove-cma-validation

OCPBUGS28861 Remove CMA log validation statements

Author: mburke5678

nodes/cma/nodes-cma-autoscaling-custom-about.adoc

@@ -28,17 +28,3 @@ The activation value has more priority than the scaling value in case of differe
 You can create only one scaled object or scaled job for each workload that you want to scale. Also, you cannot use a scaled object or scaled job and the horizontal pod autoscaler (HPA) on the same workload. If you want to scale based on a custom trigger and CPU/Memory, you can create multiple triggers in the scaled object or scaled job.
 ====
 ////
-
-You can verify that the autoscaling has taken place by reviewing the number of pods in your custom resource or by reviewing the Custom Metrics Autoscaler Operator logs for messages similar to the following:
-
-[source,terminal]
-----
-Successfully set ScaleTarget replica count
-----
-
-[source,terminal]
-----
-Successfully updated ScaleTarget
-----
-
-You can temporarily pause the autoscaling of a workload object, if needed. For example, you could pause autoscaling before performing cluster maintenance.

nodes/cma/nodes-cma-autoscaling-custom.adoc

@@ -36,20 +36,6 @@ You can create only one scaled object or scaled job for each workload that you w
 ====
 ////
 
-You can verify that the autoscaling has taken place by reviewing the number of pods in your custom resource or by reviewing the Custom Metrics Autoscaler Operator logs for messages similar to the following:
-
-[source,terminal]
-----
-Successfully set ScaleTarget replica count
-----
-
-[source,terminal]
-----
-Successfully updated ScaleTarget
-----
-
-You can temporarily pause the autoscaling of a workload object, if needed. For example, you could pause autoscaling before performing cluster maintenance.
-
 .Custom metrics autoscaler workflow
 image::564_OpenShift_Custom_Metrics_Autoscaler_0224.png[Custom metrics autoscaler workflow]
 
@@ -66,4 +52,3 @@ image::564_OpenShift_Custom_Metrics_Autoscaler_0224.png[Custom metrics autoscale
 6. As a it operates, a workload can affect the scaling metrics. For example, if a workload is scaled up to handle work in a Kafka queue, the queue size decreases after the workload processes all the work. As a result, the workload is scaled down.
 
 7. If the metrics are in a range specified by the `minReplicaCount` value, the custom metrics autoscaler controller disables all scaling, and leaves the replica count at a fixed level. If the metrics exceed that range, the custom metrics autoscaler controller enables scaling and allows the HPA to scale the workload. While scaling is disabled, the HPA does not take any action.
-