Learn about the things to know about Azure Virtual Machine Autoscaling

Azure Virtual Machine Autoscaling is a powerful feature that automatically adjusts the number of virtual machines (VMs) in a Virtual Machine Scale Set (VMSS) based on demand, improving performance while managing costs.

Autoscaling is crucial for applications with fluctuating workloads because it ensures that you have enough VMs to handle traffic spikes and saves costs during low-demand periods.

Here are the key things you should know about Azure VM Autoscaling.

What is Azure VM Autoscaling?

Azure VM autoscaling is a feature of Virtual Machine Scale Sets (VMSS) that automatically adjusts the number of VM instances in the scale set based on predefined metrics, ensuring optimal resource usage and performance.

It allows you to automatically increase or decrease the number of VMs in response to real-time application demand or predefined schedules.

Key Components of Azure Autoscaling

1. Virtual Machine Scale Set (VMSS): A set of identical VMs that can scale in or out to meet demand.

2. Scaling Metrics: Metrics that trigger scaling actions. Commonly used metrics include:

3. CPU utilization

4. Memory usage

5. Disk I/O

6. Network throughput

7. Custom metrics (e.g., request count, queue length)

8. Scaling Rules: These define how scaling actions are triggered. Rules can be based on the metrics you define (e.g., scale out when CPU > 75%, scale in when CPU < 30%).

9. Minimum and Maximum Instances: You can set a minimum and maximum number of instances in the scale set. The system will scale the instances between these limits.

How Azure VM Autoscaling Works

Autoscale Settings

To enable autoscaling, you configure the VMSS with autoscaling policies based on metrics or schedule.

Azure’s Azure Monitor tracks the chosen metrics.

Scaling Triggers

When a defined metric crosses a threshold (e.g., CPU usage > 80% for 5 minutes), Azure triggers a scaling action (either scale-out or scale-in).

Scaling Actions

1. Scale Out: Azure adds more VM instances to handle increased load.

2. Scale In: Azure removes VM instances when demand decreases.

3. Cool-down Period: After a scaling action (either scale-out or scale-in), a cool-down period helps to prevent excessive scaling actions within a short period.

Types of Scaling Actions

Horizontal Scaling (Scale-Out / Scale-In)

Adding or removing VM instances in a VMSS based on demand.

This is the most common type of autoscaling and is used to handle fluctuations in load.

For example, if traffic increases and the CPU utilization goes above 75%, the system can automatically add more VMs to handle the increased load.

When the load drops, fewer VMs are required, and the system will scale in.

Vertical Scaling

This involves adjusting the size of the individual VMs (e.g., changing the VM type to a more powerful size).

While this is less common in VMSS (since it is primarily designed for horizontal scaling), it can still be configured for Azure Virtual Machines individually, but not for scale sets in the same way.

Setting Up Azure VM Autoscaling

Here’s a step-by-step overview of how to set up autoscaling in Azure:

Create a Virtual Machine Scale Set

1. In the Azure portal, go to Create a resource > Compute > Virtual Machine Scale Set.

2. Choose the image, size, and configuration for the scale set.

Enable Autoscaling

1. In the Scaling tab of your VMSS, enable Autoscaling.

2. Set the minimum and maximum instance count for your scale set.

   • For instance, you may set a minimum of 1 VM and a maximum of 10 VMs.

Define Autoscale Rules

1. Choose the metric that will trigger scaling actions (e.g., CPU, memory, disk, or custom metrics).

2. Define the scaling thresholds (e.g., scale out when CPU > 80% for 5 minutes).

3. Set cool-down periods to prevent rapid scaling actions (e.g., 5 minutes).

Set Scaling Actions

1. Scale Out: Increase the number of VMs when the metric exceeds the threshold.

2. Scale In: Decrease the number of VMs when the metric drops below the threshold.

3. Adjust these rules as needed to optimize resource allocation.

Key Features and Options for VM Autoscaling

Custom Metrics

You can define custom metrics to trigger scaling actions.

For example, use Azure Application Insights to monitor the queue length in an application and scale based on the number of requests waiting to be processed.

Scheduled Scaling

For predictable workloads (e.g., weekly load patterns), you can configure scheduled scaling.

This allows you to set scaling actions for specific time periods.

For example, scale out in the evening when user activity is higher and scale in overnight when activity is lower.

Priority Scaling

Use instance priority to define which VMs should be scaled in or out first.

You can prioritize instances based on instance health or performance.

Scaling Based on Multiple Metrics

Azure allows scaling to be triggered based on multiple metrics.

For example, you could scale out if both CPU and memory usage exceed thresholds simultaneously.

This gives you more granular control over when scaling occurs.

Best Practices for Azure VM Autoscaling

Set Minimum and Maximum Instance Count

Always define a minimum number of VMs to ensure your application always has enough capacity to handle some level of traffic.

Similarly, set a maximum number of VMs to prevent over-provisioning and unnecessary costs.

Choose Relevant Metrics

Choose metrics that truly reflect the load on your application.

CPU and memory are common choices, but custom metrics (such as application request count) can often provide more meaningful insights.

Avoid Over-reliance on CPU

CPU alone may not be a good indicator of application load.

For example, an application could be I/O-bound, not CPU-bound.

Test Scaling Configurations

Before relying on autoscaling in production, simulate traffic spikes or load conditions to ensure that scaling happens as expected.

Monitor the application’s performance during these tests.

Use Azure Monitor to track scaling operations and ensure scaling actions occur without degrading performance.

Configure Cool-down Periods

Set appropriate cool-down periods to avoid excessive scaling actions.

If you scale in too quickly after scaling out, you may create instability in your application or waste resources.

Monitor and Adjust

Continuously monitor scaling performance and adjust scaling thresholds and metrics over time.

Cloud workloads can change dynamically, and your autoscaling rules should evolve with your application.

Handle Dependencies Gracefully

Ensure that any dependent services (e.g., databases, external APIs) are also scaled appropriately or can handle increased traffic when your VMs scale out.

Azure VM Autoscaling Limitations

Scale Set Limits

There are limits on the number of VMs in a scale set.

While VMSS can support up to 1,000 instances, the actual limits may depend on your subscription and region.

Stateful Applications

Autoscaling is best suited for stateless applications, where VMs can be added or removed without disrupting the application’s function.

Stateful applications, where the VM stores persistent state or data, may require additional configuration to handle scaling without data loss or corruption.

Regional Availability

Autoscaling may be limited or behave differently depending on the Azure region you deploy in, so always check for regional constraints and availability before implementing autoscaling.

Monitoring and Troubleshooting VM Autoscaling

Azure Monitor

Use Azure Monitor to track key metrics like CPU, memory, and disk usage.

Set up alerts to notify you when scaling actions occur or when thresholds are reached.

Autoscale History

Review the autoscale history in the Azure Portal to see when scaling actions occurred and whether they were triggered by the right metrics.

This helps troubleshoot and optimize your scaling policies.

Scaling Logs

Check the diagnostic logs to see the details of scaling events, such as which metrics caused the scale-out or scale-in actions and whether the scaling actions were effective in meeting performance requirements.

Summary

Azure Virtual Machine Autoscaling is a powerful and flexible way to manage application performance and resource costs dynamically.

By configuring scaling policies based on metrics, setting minimum and maximum instance limits, and using custom scaling rules, you can ensure that your application remains highly available, responsive, and cost-efficient.

However, to fully leverage autoscaling, it's important to carefully monitor your scaling configuration, test it under real-world conditions, and continuously optimize it as your application and workload evolve.