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Copy file name to clipboardExpand all lines: articles/azure-monitor/autoscale/autoscale-overview.md
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@@ -7,7 +7,7 @@ ms.service: azure-monitor
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ms.subservice: autoscale
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ms.topic: conceptual
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ms.date: 08/01/2022
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ms.reviewer: riroloff
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ms.reviewer:
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---
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Azure autoscale supports many resource types. For more information about supported resources, see [autoscale supported resources](#supported-services-for-autoscale).
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> [!NOTE]
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> [Availability sets](/archive/blogs/kaevans/autoscaling-azurevirtual-machines) are an older scaling feature for virtual machines with limited support. We recommend migrating to [virtual machine scale sets](../../virtual-machine-scale-sets/overview.md) for faster and more reliable autoscale support.
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> [Availability sets](/archive/blogs/kaevans/autoscaling-azurevirtual-machines) are an older scaling feature for virtual machines with limited support. We recommend migrating to [virtual machine scale sets](/azure/virtual-machine-scale-sets/overview) for faster and more reliable autoscale support.
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## What is autoscale
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Autoscale is a service that allows you to automatically add and remove resources according to the load on your application.
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When your application experiences higher load, autoscale adds resources to handle the increased load. When load is low, autoscale reduces the number of resources, lowering your costs. You can scale your application based on metrics like CPU usage, queue length, and available memory, or based on a schedule. Metrics and schedules are set up in rules. The rules include a minimum level of resources that you need to run your application, and a maximum level of resources that won't be exceeded.
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When your application experiences higher load, autoscale adds resources to handle the increased load. When load is low, autoscale reduces the number of resources, lowering your costs. You can scale your application based on metrics like CPU usage, queue length, and available memory, or based on a schedule. Metrics and schedules are set up in rules. The rules include a minimum level of resources that you need to run your application, and a maximum level of resources that won't be exceeded.
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For example, scale out your application by adding VMs when the average CPU usage per VM is above 70%. Scale it back in removing VMs when CPU usage drops to 40%.
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:::image type="content" source="./media/autoscale-overview/vertical-scaling.png" alt-text="A diagram that shows scaling up by adding CPU and memory to a virtual machine.":::
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When the conditions in the rules are met, one or more autoscale actions are triggered, adding or removing VMs. In addition, you can perform other actions like sending email notifications, or webhooks to trigger processes in other systems.
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### Predictive autoscale (preview)
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[Predictive autoscale](./autoscale-predictive.md) uses machine learning to help manage and scale Azure virtual machine scale sets with cyclical workload patterns. It forecasts the overall CPU load on your virtual machine scale set, based on historical CPU usage patterns. The scale set can then be scaled out in time to meet the predicted demand.
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### Resource metrics
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Resources generate metrics that are used in autoscale rules to trigger scale events. Virtual machine scale sets use telemetry data from Azure diagnostics agents to generate metrics. Telemetry for Web apps and Cloud services comes directly from the Azure Infrastructure.
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Some commonly used metrics include CPU usage, memory usage, thread counts, queue length, and disk usage. See [Autoscale Common Metrics](autoscale-common-metrics.md) for a list of available metrics.
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Resources generate metrics that are used in autoscale rules to trigger scale events. Virtual machine scale sets use telemetry data from Azure diagnostics agents to generate metrics. Telemetry for Web apps and Cloud services comes directly from the Azure Infrastructure. Some commonly used metrics include CPU usage, memory usage, thread counts, queue length, and disk usage. See [Autoscale Common Metrics](autoscale-common-metrics.md) for a list of available metrics.
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### Custom metrics
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Use your own custom metrics that your application generates. Configure your application to send metrics to [Application Insights](../app/app-insights-overview.md) so you can use those metrics decide when to scale.
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Use your own custom metrics that your application generates. Configure your application to send metrics to [Application Insights](/azure/azure-monitor/app/app-insights-overview) so you can use those metrics decide when to scale.
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### Time
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Rules define the conditions needed to trigger a scale event, the direction of the scaling, and the amount to scale by. Rules can be:
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+ Metric-based
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Trigger based on a metric value, for example when CPU usage is above 50%.
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+ Time-based
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Trigger based on a schedule, for example, every Saturday at 8am.
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* Metric-based
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Trigger based on a metric value, for example when CPU usage is above 50%.
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* Time-based
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Trigger based on a schedule, for example, every Saturday at 8am.
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You can combine multiple rules using different metrics, for example CPU usage and queue length.
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+ The OR operator is used when scaling out with multiple rules.
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+ The AND operator is used when scaling in with multiple rules.
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Autoscale scales out if *any* of the rules are met, whereas autoscale scales in only if *all* the rules are met.
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In terms of logic operators, the OR operator is used when scaling out with multiple rules. The AND operator is used when scaling in with multiple rules.
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### Actions and automation
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Rules can trigger one or more actions. Actions include:
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+ Scale - Scale resources in or out.
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+ Email - Send an email to the subscription admins, co-admins, and/or any other email address.
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+ Webhooks - Call webhooks to trigger multiple complex actions inside or outside Azure. In Azure, you can:
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+ Start an [Azure Automation runbook](../../automation/overview.md).
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+Call an [Azure Function](../../azure-functions/functions-overview.md).
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+Trigger an [Azure Logic App](../../logic-apps/logic-apps-overview.md).
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* Scale - Scale resources in or out.
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* Email - Send an email to the subscription admins, co-admins, and/or any other email address.
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* Webhooks - Call webhooks to trigger multiple complex actions inside or outside Azure. In Azure, you can:
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* Start an [Azure Automation runbook](/azure/automation/overview).
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*Call an [Azure Function](/azure/azure-functions/functions-overview).
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*Trigger an [Azure Logic App](/azure/logic-apps/logic-apps-overview).
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## Autoscale settings
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For code examples, see
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+[Advanced Autoscale configuration using Resource Manager templates for virtual machine scale sets](autoscale-virtual-machine-scale-sets.md)
Autoscale scales horizontally, which is an increase, or decrease of the number of resource instances. For example, in a virtual machine scale set, scaling out means adding more virtual machines. Scaling in means removing virtual machines. Horizontal scaling is flexible in a cloud situation as it allows you to run a large number of VMs to handle load.
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Autoscale scales horizontally, which is an increase, or decrease of the number of resource instances. For example, in a virtual machine scale set, scaling out means adding more virtual machines Scaling in means removing virtual machines. Horizontal scaling is flexible in a cloud situation as it allows you to run a large number of VMs to handle load.
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In contrast, vertical scaling, keeps the same number of resources constant, but gives them more capacity in terms of memory, CPU speed, disk space and network. Adding or removing capacity in vertical scaling is known as scaling or down. Vertical scaling is limited by the availability of larger hardware, which eventually reaches an upper limit. Hardware size availability varies in Azure by region. Vertical scaling may also require a restart of the virtual machine during the scaling process.
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| Azure Data Explorer Clusters|[Manage Azure Data Explorer clusters scaling to accommodate changing demand](/azure/data-explorer/manage-cluster-horizontal-scaling)|
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| Azure Stream Analytics |[Autoscale streaming units (Preview)](../../stream-analytics/stream-analytics-autoscale.md)|
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| Azure Machine Learning Workspace |[Autoscale an online endpoint](../../machine-learning/how-to-autoscale-endpoints.md)|
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| Azure SignalR Service |[Automatically scale units of an Azure SignalR service](../../azure-signalr/signalr-howto-scale-autoscale.md)|
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| Logic apps |[Adding integration service environment (ISE) capacity](../../logic-apps/ise-manage-integration-service-environment.md#add-ise-capacity)|
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| Media Services |[Autoscaling in Media Services](/azure/media-services/latest/release-notes#autoscaling)|
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| Service Bus |[Automatically update messaging units of an Azure Service Bus namespace](../../service-bus-messaging/automate-update-messaging-units.md)|
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| Spring Cloud |[Set up autoscale for microservice applications](../../spring-apps/how-to-setup-autoscale.md)|
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| Service Fabric Managed Clusters |[Introduction to Autoscaling on Service Fabric managed clusters](../../service-fabric/how-to-managed-cluster-autoscale.md)|
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## Next steps
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To learn more about autoscale, see the following resources:
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+[Azure Monitor autoscale common metrics](autoscale-common-metrics.md)
+[Autoscale using Resource Manager templates for virtual machine scale sets](../../virtual-machine-scale-sets/tutorial-autoscale-powershell.md?toc=%2fazure%2fazure-monitor%2ftoc.json)
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+[Best practices for Azure Monitor autoscale](autoscale-best-practices.md)
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+[Use autoscale actions to send email and webhook alert notifications](autoscale-webhook-email.md)
*[Autoscale using Resource Manager templates for virtual machine scale sets](../../virtual-machine-scale-sets/tutorial-autoscale-powershell.md?toc=%2fazure%2fazure-monitor%2ftoc.json)
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*[Best practices for Azure Monitor autoscale](autoscale-best-practices.md)
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*[Use autoscale actions to send email and webhook alert notifications](autoscale-webhook-email.md)
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