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Autoscaling is a cloud system or process that adjusts available resources based on the current demand.
Autoscaling performs scaling in and out, as opposed to scaling up and down.
Autoscaling provides elasticity for your services.
Autoscaling responds to changes in the environment by adding or removing web servers and balancing the load between them.
Autoscaling doesn't have any effect on the CPU power, memory, or storage capacity of the web servers powering the app, it only changes the number of web servers.
A rule specifies the threshold for a metric, and triggers an autoscale event when this threshold is crossed.
Autoscaling can also deallocate resources when the workload has diminished.
To prevent runaway autoscaling, an App Service Plan has an instance limit.
Plans in more expensive pricing tiers have a higher limit.
Autoscaling cannot create more instances than this limit.
Azure provides two options for autoscaling:
Scale based on a metric, such as the length of the disk queue, or the number of HTTP requests awaiting processing.
Scale to a specific instance count according to a schedule. For example, you can arrange to scale out at a particular time of day, or on a specific date or day of the week.
If you need to scale out incrementally, you can combine metric and schedule-based autoscaling in the same autoscale condition.
You can also scale based on metrics for other Azure services.
For example, if the web app processes requests received from a Service Bus Queue, you might want to spin up additional instances of a web app if the number of items held in an Azure Service Bus Queue exceeds a critical length.
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Not all pricing tiers support autoscaling. The development pricing tiers are either limited to a single instance (the F1 and D1 tiers), or they only provide manual scaling (the B1 tier). If you've selected one of these tiers, you must first scale up to the S1 or any of the P level production tiers |
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How Autoscale Analyzes Metrics
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