Merge pull request #208215 from ritaroloff/main

Update predictive autoscale

Author: prmerger-automator[bot]

articles/azure-monitor/autoscale/autoscale-predictive.md

@@ -19,24 +19,11 @@ Predictive autoscale adheres to the scaling boundaries you've set for your virtu
 
 *Forecast only* allows you to view your predicted CPU forecast without triggering the scaling action based on the prediction. You can then compare the forecast with your actual workload patterns to build confidence in the prediction models before you enable the predictive autoscale feature.
 
-## Public preview support, availability, and limitations
+## Public preview support and limitations
 
 >[!NOTE]
 > This release is a public preview. We're testing and gathering feedback for future releases. As such, we do not provide production-level support for this feature. Support is best effort. Send feature suggestions or feedback on predicative autoscale to [email protected].
 
-During public preview, predictive autoscale is only available in the following regions:
-
-- West Central US
-- West US2
-- UK South
-- UK West
-- Southeast Asia
-- East Asia
-- Australia East
-- Australia South east
-- Canada Central
-- Canada East
-
 The following limitations apply during public preview. Predictive autoscale:
 
 - Only works for workloads exhibiting cyclical CPU usage patterns.
@@ -313,6 +300,9 @@ For more information on Azure Resource Manager templates, see [Resource Manager
 
 This section answers common questions.
 
+### Why is CPU percentage over 100 percent on predictive charts?
+The predictive chart shows the cumulative load for all machines in the scale set. If you have 5 VMs in a scale set, the maximum cumulative load for all VMs will be 500%, that is, five times the 100% maximum CPU load of each VM. 
+
 ### What happens over time when you turn on predictive autoscale for a virtual machine scale set?
 
 Prediction autoscale uses the history of a running virtual machine scale set. If your scale set has been running less than 7 days, you'll receive a message that the model is being trained. For more information, see the [no predictive data message](#errors-and-warnings). Predictions improve as time goes by and achieve maximum accuracy 15 days after the virtual machine scale set is created.
@@ -327,6 +317,11 @@ The modeling works best with workloads that exhibit periodicity. We recommend th
 
 Standard autoscaling is a necessary fallback if the predictive model doesn't work well for your scenario. Standard autoscale will cover unexpected load spikes, which aren't part of your typical CPU load pattern. It also provides a fallback if an error occurs in retrieving the predictive data.
 
+### Which rule will take effect if both predictive and standard autoscale rules are set?
+Standard autoscale rules are used if there is an unexpected spike in the CPU load, or an error occurs when retrieving predictive data```
+
+We use the threshold set in the standard autoscale rules to understand when you’d like to scale out and by how many instances. If you want your VM scale set to scale out when the CPU usage exceeds 70%, and actual or predicted data shows that CPU usage is or will be over 70%, then a scale out will occur.
+
 ## Errors and warnings
 
 This section addresses common errors and warnings.
@@ -361,4 +356,4 @@ Learn more about autoscale in the following articles:
 - [Azure Monitor autoscale common metrics](./autoscale-common-metrics.md)
 - [Best practices for Azure Monitor autoscale](./autoscale-best-practices.md)
 - [Use autoscale actions to send email and webhook alert notifications](./autoscale-webhook-email.md)
-- [Autoscale REST API](/rest/api/monitor/autoscalesettings)
+- [Autoscale REST API](/rest/api/monitor/autoscalesettings)