Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into privatelink-disable-policy

Author: asudbring

.openpublishing.redirection.azure-monitor.json

@@ -5701,6 +5701,11 @@
             "source_path_from_root": "/articles/azure-monitor/autoscale/autoscale-resource-log-schema.md",
             "redirect_url": "/azure/azure-monitor/autoscale/autoscale-diagnostics",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/azure-monitor/alerts/proactive-performance-diagnostics.md",
+            "redirect_url": "https://azure.microsoft.com/updates/public-preview-alerts-based-smart-detection-for-application-insights/",
+            "redirect_document_id": false
         }
     ]
 }

articles/azure-monitor/alerts/media/proactive-performance-diagnostics/03.png renamed to articles/azure-monitor/alerts/media/smart-detection-performance/03.png

@@ -192,4 +192,4 @@ Learn more about automatically detecting:
 
 - [Failure anomalies](./proactive-failure-diagnostics.md)
 - [Memory Leaks](./proactive-potential-memory-leak.md)
-- [Performance anomalies](./proactive-performance-diagnostics.md)
+- [Performance anomalies](./smart-detection-performance.md)

articles/azure-monitor/alerts/media/proactive-performance-diagnostics/dependency_duration_degradation.png renamed to articles/azure-monitor/alerts/media/smart-detection-performance/dependency_duration_degradation.png

@@ -36,7 +36,7 @@ Select a detection to view its details.
 Smart detection detects and notifies about various issues, such as:
 
 * [Smart detection - Failure Anomalies](./proactive-failure-diagnostics.md). We use machine learning to set the expected rate of failed requests for your app, correlating with load, and other factors. Notifies if the failure rate goes outside the expected envelope.
-* [Smart detection - Performance Anomalies](./proactive-performance-diagnostics.md). Notifies if response time of an operation or dependency duration is slowing down, compared to historical baseline. It also notifies if we identify an anomalous pattern in response time, or page load time.   
+* [Smart detection - Performance Anomalies](./smart-detection-performance.md). Notifies if response time of an operation or dependency duration is slowing down, compared to historical baseline. It also notifies if we identify an anomalous pattern in response time, or page load time.   
 * General degradations and issues, like [Trace degradation](./proactive-trace-severity.md), [Memory leak](./proactive-potential-memory-leak.md), [Abnormal rise in Exception volume](./proactive-exception-volume.md) and [Security anti-patterns](./proactive-application-security-detection-pack.md).
 
 (The help links in each notification take you to the relevant articles.)

articles/azure-monitor/alerts/media/proactive-performance-diagnostics/server_response_time_degradation.png renamed to articles/azure-monitor/alerts/media/smart-detection-performance/server_response_time_degradation.png

@@ -43,5 +43,5 @@ Learn more about Smart Detection:
 
 - [Failure anomalies](./proactive-failure-diagnostics.md)
 - [Memory Leaks](./proactive-potential-memory-leak.md)
-- [Performance anomalies](./proactive-performance-diagnostics.md)
+- [Performance anomalies](./smart-detection-performance.md)
 

articles/azure-monitor/alerts/media/proactive-performance-diagnostics/smart_detection_configuration.png renamed to articles/azure-monitor/alerts/media/smart-detection-performance/smart_detection_configuration.png

@@ -415,7 +415,7 @@ Smart Detection of Failure Anomalies complements other similar but distinct feat
 
 * [metric alerts](./alerts-log.md) are set by you and can monitor a wide range of metrics such as CPU occupancy, request rates,  page load times, and so on. You can use them to warn you, for example, if you need to add more resources. By contrast, Smart Detection of Failure Anomalies covers a small range of critical metrics (currently only failed request rate), designed to notify you in near real-time manner once your web app's failed request rate increases compared to web app's normal behavior. Unlike metric alerts, Smart Detection automatically sets and updates thresholds in response changes in the behavior. Smart Detection also starts the diagnostic work for you, saving you time in resolving issues.
 
-* [Smart Detection of performance anomalies](proactive-performance-diagnostics.md) also uses machine intelligence to discover unusual patterns in your metrics, and no configuration by you is required. But unlike Smart Detection of Failure Anomalies, the purpose of Smart  Detection of performance anomalies is to find segments of your usage manifold that might be badly served - for example, by specific pages on a specific type of browser. The analysis is performed daily, and if any result is found, it's likely to be much less urgent than an alert. By contrast, the analysis for Failure Anomalies is performed continuously on incoming application data, and you will be notified within minutes if server failure rates are greater than expected.
+* [Smart Detection of performance anomalies](smart-detection-performance.md) also uses machine intelligence to discover unusual patterns in your metrics, and no configuration by you is required. But unlike Smart Detection of Failure Anomalies, the purpose of Smart  Detection of performance anomalies is to find segments of your usage manifold that might be badly served - for example, by specific pages on a specific type of browser. The analysis is performed daily, and if any result is found, it's likely to be much less urgent than an alert. By contrast, the analysis for Failure Anomalies is performed continuously on incoming application data, and you will be notified within minutes if server failure rates are greater than expected.
 
 ## If you receive a Smart Detection alert
 *Why have I received this alert?*

articles/azure-monitor/alerts/proactive-arm-config.md

@@ -36,7 +36,7 @@ No, a notification doesn't mean that your app definitely has a problem. It's sim
 The notifications include diagnostic information. Here's an example:
 
 
-![Here is an example of Server Response Time Degradation detection](media/proactive-performance-diagnostics/server_response_time_degradation.png)
+![Here is an example of Server Response Time Degradation detection](media/smart-detection-performance/server_response_time_degradation.png)
 
 1. **Triage**. The notification shows you how many users or how many operations are affected. This information can help you assign a priority to the problem.
 2. **Scope**. Is the problem affecting all traffic, or just some pages? Is it restricted to particular browsers or locations? This information can be obtained from the notification.
@@ -48,7 +48,7 @@ The notifications include diagnostic information. Here's an example:
 
 Smart detection notifications are enabled by default. They are sent to users that have [Monitoring Reader](../../role-based-access-control/built-in-roles.md#monitoring-reader) and [Monitoring Contributor](../../role-based-access-control/built-in-roles.md#monitoring-contributor) access to the subscription in which the Application Insights resource resides. To change the default notification, either click **Configure** in the email notification, or open **Smart detection settings** in Application Insights. 
 
-  ![Smart Detection Settings](media/proactive-performance-diagnostics/smart_detection_configuration.png)
+  ![Smart Detection Settings](media/smart-detection-performance/smart_detection_configuration.png)
 
   * You can disable the default notification, and replace it with a specified list of emails.
 
@@ -130,7 +130,7 @@ Modern applications often adopt a micro services design approach, which in many
 
 Example of dependency degradation notification:
 
-![Here is an example of Dependency Duration Degradation detection](media/proactive-performance-diagnostics/dependency_duration_degradation.png)
+![Here is an example of Dependency Duration Degradation detection](media/smart-detection-performance/dependency_duration_degradation.png)
 
 Notice that it tells you:
 
@@ -146,18 +146,18 @@ Notice that it tells you:
 
 ## Smart detection of slow performing patterns 
 
-Application Insights finds performance issues that might only affect some portion of your users, or only affect users in some cases. For example, if a page loads slower on a specific browser types compared to others,  or if a particular server handles requests more slowly than other servers. It can also discover problems that are associated with combinations of properties, such as slow page loads in one geographical area for clients using particular operating system.  
+Application Insights finds performance issues that might only affect some portion of your users, or only affect users in some cases. For example, if a page loads slower on a specific browser type compared to others,  or if a particular server handles requests more slowly than other servers. It can also discover problems that are associated with combinations of properties, such as slow page loads in one geographical area for clients using particular operating system.  
 
 Anomalies like these are hard to detect just by inspecting the data, but are more common than you might think. Often they only surface when your customers complain. By that time, it's too late: the affected users are already switching to your competitors!
 
 Currently, our algorithms look at page load times, request response times at the server, and dependency response times.  
 
 You don't have to set any thresholds or configure rules. Machine learning and data mining algorithms are used to detect abnormal patterns.
 
-![From the email alert, click the link to open the diagnostic report in Azure](./media/proactive-performance-diagnostics/03.png)
+![From the email alert, click the link to open the diagnostic report in Azure](./media/smart-detection-performance/03.png)
 
 * **When** shows the time the issue was detected.
-* **What** describes te problem that was detected, and th characteristics of the set of events that we found, which displayed the problem behavior.
+* **What** describes the problem that was detected, and th characteristics of the set of events that we found, which displayed the problem behavior.
 * The table compares the poorly performing set with the average behavior of all other events.
 
 Click the links to open Metric Explorer to view reports, filtered by the time and properties of the slow performing set.