Merge pull request #71651 from openshift/revert-71467-cma-new-figure

Revert "OSDOCS-9049: Custom Metrics Autoscaler add new image"

Author: mburke5678

images/564_OpenShift_Custom_Metrics_Autoscaler_0224.png

@@ -104,8 +104,7 @@ spec:
       policy:
         rules:
         - level: Metadata
-        omitStages:
-         - "RequestReceived"
+        omitStages: ["RequestReceived"]
         omitManagedFields: false
       lifetime:
         maxAge: "2"

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

@@ -118,8 +118,7 @@ spec:
       policy:
         rules:
         - level: Metadata
-        omitStages:
-         - "RequestReceived"
+        omitStages: ["RequestReceived"]
         omitManagedFields: false
       lifetime:
         maxAge: "2"

modules/sd-nodes-cma-autoscaling-custom-install.adoc

@@ -16,7 +16,7 @@ The custom metrics autoscaler currently supports only the Prometheus, CPU, memor
 
 The Custom Metrics Autoscaler Operator scales your pods up and down based on custom, external metrics from specific applications. Your other applications continue to use other scaling methods. You configure _triggers_, also known as scalers, which are the source of events and metrics that the custom metrics autoscaler uses to determine how to scale. The custom metrics autoscaler uses a metrics API to convert the external metrics to a form that {product-title} can use. The custom metrics autoscaler creates a horizontal pod autoscaler (HPA) that performs the actual scaling.
 
-To use the custom metrics autoscaler, you create a `ScaledObject` or `ScaledJob` object for a workload, which is a custom resource (CR) that defines the scaling metadata. You specify the deployment or job to scale, the source of the metrics to scale on (trigger), and other parameters such as the minimum and maximum replica counts allowed.
+To use the custom metrics autoscaler, you create a `ScaledObject` or `ScaledJob` object, which is a custom resource (CR) that defines the scaling metadata. You specify the deployment or job to scale, the source of the metrics to scale on (trigger), and other parameters such as the minimum and maximum replica counts allowed.
 
 [NOTE]
 ====
@@ -49,21 +49,3 @@ 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]
-
-1. You create or modify a scaled object custom resource for a workload on a cluster. The object contains the scaling configuration for that workload. Prior to accepting the new object, the OpenShift API server sends it to the custom metrics autoscaler admission webhooks process to ensure that the object is valid. If validation succeeds, the API server persists the object.
-
-2. The custom metrics autoscaler controller watches for new or modified scaled objects. When the OpenShift API server notifies the controller of a change, the controller monitors any external trigger sources, also known as data sources, that are specified in the object for changes to the metrics data. One or more scalers request scaling data from the external trigger source. For example, for a Kafka trigger type, the controller uses the Kafka scaler to communicate with a Kafka instance to obtain the data requested by the trigger.
-
-3. The controller creates a horizontal pod autoscaler object for the scaled object. As a result, the Horizontal Pod Autoscaler (HPA) Operator starts monitoring the scaling data associated with the trigger. The HPA requests scaling data from the cluster OpenShift API server endpoint.
-
-4. The OpenShift API server endpoint is served by the custom metrics autoscaler metrics adapter. When the metrics adapter receives a request for custom metrics, it uses a GRPC connection to the controller to request it for the most recent trigger data received from the scaler.
-
-5. The HPA makes scaling decisions based upon the data received from the metrics adapter and scales the workload up or down by increasing or decreasing the replicas.
-
-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.
-