What is Autoscale
- Provides resources to handle the load on your application
- Saves money by removing resources when not in use.
- Scales horizontally i.e. increase or decrease number of VM instances and more flexible in cloud environment.
- Two ways to trigger the autoscale for the supported services and these are Matrix-Based and Time-Based rules.
- Autoscaling leverages the elasticity of cloud-hosted environments while easing management overhead
Use case of time-based autoscaling
- If you could predict your application’s demand based on the its usage, you can scale out the instances during the high-demand window and scale in the instances when least used.
- Development environments are used only during weekday business hours. The Time-Based autoscaling settings adds the development environments only when they are needed and automatically shut down the instances when they are not needed.
Use case of matrix-based autoscaling
- Caching servers like Redis on virtual machines, you can configure the Metrics-Based autoscaling rules based on memory utilization.
- Web servers that are CPU intensive, so based on CPU usages Metrics-Based autoscaling rules to scale out the instances during unexpected demands.
Autoscale services in Azure
- API Management service
- Azure App Service
- Azure Data Explorer Clusters
- Cloud Services
- Logic Apps
- Service Bus
- Spring Cloud
- SignalR Service
- Virtual Machines: Classic
- Virtual Machines: Windows Scale Sets
- Virtual Machines: Linux Scale Sets
- Virtual Machines: Windows
- Web Apps
Note – Click on link to go Microsoft Documentation.
This is a quick reference of autoscale services supported in Azure with some usecases. In your service, depends on load, consumption and when you need it, you can configure rules for Matrix-Based or Time- Based autoscaling.
If you have any suggestions/feedback, please put them in the comment box.
Happy Learning 🙂
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