How Azure AutoScaling driven up by Azure Monitor Metrics

 

Autoscale is a built-in feature of Cloud Services, Mobile Services, Virtual Machine Scale Sets and Websites that helps applications perform their best when demand changes. Azure Autoscaling driven up by Azure Monitor Metrics data which is collected from Azure Services.

Azure Monitor Metrics is a feature of Azure Monitor that collects numeric data from monitored resources into a time series database. Metrics are numerical values that are collected at regular intervals and describe some aspect of a system at a particular time.

Azure Monitor collects metrics from the following sources.

1. Azure resources
2. Applications
3. Virtual machine agents
4. Custom metrics

Data that Azure Monitor Metrics collects is stored in a time-series database that's optimized for analyzing time-stamped data. Each set of metric values is a time series with the following properties:

1. The time that the value was collected.
2. The resource that the value is associated with.
3. A namespace that acts like a category for the metric.
4. A metric name.
5. The value itself.

Autoscale allows you to have the right amount of resources running to handle the load on your application. It allows you to add resources to handle increases in load and also save money by removing resources that are sitting idle. You specify a minimum and maximum number of instances to run and add or remove VMs automatically based on a set of rules. Having a minimum makes sure your application is always running even under no load. Having a maximum limits your total possible hourly cost. You automatically scale between these two extremes using rules you create.

You can implement autoscaling based on schedule(TIME) rules which are based on UTC and You can also  create complex overlapping rules as needed for your situation. Metric-based rules measure application load and add or remove VMs based on that load. Schedule-based rules allow you to scale when you see time patterns in your load and want to scale before a possible load increase or decrease occurs.

Use the following best practices as you use autoscale.

1. Ensure the maximum and minimum values are different and have an adequate margin between them.
2. Manual scaling is reset by autoscale min and max.
3. Always use a scale-out and scale-in rule combination that performs an increase and decrease.
4. Choose the appropriate statistic for your diagnostics metric.
5. Choose the thresholds carefully for all metric types.

Ref :- 

https://docs.microsoft.com/

https://portal.azure.com

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                                                                                                                                                                        Alok Kumar