What is Azure Autoscaling and How Does It Work?

What is Autoscaling?

Autoscaling is a technique used to automatically adjust the number of active resources in the cloud. You can autoscale resources such as virtual machines, servers, and containers based on your current demand and workloads. It helps you to tackle peak demand by provisioning additional resources and then scaling down during low-demand periods as required. You can set up predefined policies and thresholds to automate this process.

For example, if CPU usage, memory consumption, or network traffic reaches a specific limit, new instances are automatically added to balance the load. Conversely, when usage drops below a certain threshold, instances are automatically terminated or deactivated.

Autoscaling can be applied to various cloud environments, such as AWS Auto Scaling, Google Cloud Autoscaler, and Azure Virtual Machine Scale Sets, each offering flexible options for managing resources based on custom-defined rules.

How does Azure Autoscaling Work?

Azure Autoscaling is built into the Azure platform to dynamically scale resources based on predefined policies, metrics, or schedules. It supports two primary forms of scaling:

1. Vertical Scaling: Adjusts the computing power of a single virtual machine (e.g., adding more CPU or RAM).
2. Horizontal Scaling: Adds or removes virtual machines in response to demand (e.g., increasing the number of instances to handle a spike in web traffic).

Azure provides a suite of tools to set up autoscaling policies across various services, including Virtual Machine Scale Sets, App Service Plans, and Azure Kubernetes Service (AKS). These policies are customizable based on metrics such as CPU usage, memory consumption, or incoming requests.

Azure autoscaling also supports scheduled scaling, allowing businesses to set specific times for scaling. For instance, if traffic spikes occur every Monday, autoscaling can provision additional resources in advance, enhancing both responsiveness and resource management.

Other main concepts behind Azure Autoscaling include:

• Resource Metrics: Azure resources produce various metrics that help autoscale rules decide when to adjust resources. For instance, Virtual Machine Scale Sets use telemetry data from Azure diagnostics agents to monitor metrics, while Azure App Service and Cloud Services pull telemetry data directly from Azure’s infrastructure. Commonly used metrics include CPU and memory usage, thread counts, queue length, and disk usage.
• Custom Metrics: You can also set up custom metrics specific to your application’s needs. By sending these metrics to Azure Application Insights, your application data becomes part of the scaling decisions, allowing for more precise scaling based on unique requirements.
• Time-Based Scaling: If you notice predictable load patterns, you can set up schedule-based rules. For example, if you expect a traffic spike at certain times, such as every morning, you can schedule scaling actions in advance.
• Rules: Rules are the core of autoscaling decisions. They specify when a scale action should occur, the direction (scale in or scale out), and by how much. You can set up multiple rules using different metrics, such as CPU usage and queue length, with a limit of up to 10 rules per profile. Rules come in two main types:
  • Metric-Based Rules: Trigger scaling based on specific metric thresholds (e.g., CPU usage above 50%).
  • Time-Based Rules: Trigger scaling based on a set schedule (e.g., every Saturday at 8 AM).

• Actions and Automation: Rules can initiate different actions:
  • Scale: Adjust resources by scaling them in or out.
  • Email Notifications: Send alerts to administrators and designated email addresses.
  • Webhooks: Trigger complex actions, both inside and outside of Azure. For example, you could start an Azure Automation runbook, call an Azure function, or trigger an Azure Logic App, enabling further automation.

These core concepts help Azure Autoscaling manage resources efficiently, ensuring applications stay responsive to demand while controlling costs.

How to set up Autoscaling in Azure?

Follow these simple steps to create your first autoscale setting in Azure. This example uses an App Service Plan associated with a web app.

• Open the Autoscale Pane: Go to Azure Monitor and open the Autoscale pane. Choose a resource you want to scale, such as an App Service plan. You can use an existing resource or create a new one if needed.

• Current Instance Count: Notice the initial instance count, which might be set to 1. Choose Custom Autoscale to configure your settings.
• Add Basic Details: Enter a name for your autoscale setting and select the resource group. You can use the default group or specify one.

• Choose Scaling Based on Metrics: To scale based on a specific metric, select Scale based on a metric.
• Add a Scaling Rule:
  • Click Add a rule to open the configuration pane.
  • The default rule scales up your resource by one instance if the CPU usage goes above 70%. Keep this default rule and select Add to apply it.

• Create a Scale-In Rule: For best performance, include a scale-in rule.
  • Select Add a rule again.
  • Set Operator to “Less than” and set the metric threshold to 20%.
  • Set the operation to “Decrease count by” one instance.
  • Click Add to save this rule.

• Adjust Instance Limits: Now, set limits to control the number of instances.
  • Go to Instance Limits and set the Maximum number of instances to 3.
  • Leave the Minimum at 1, which is the default.
• Save Settings: Select Save to apply the autoscale configuration.

Your autoscale setting is now complete. Here’s how it works:

• When CPU usage goes over 70%, an additional instance will automatically be added, up to a maximum of 3 instances.
• When CPU usage drops below 20%, one instance will be removed, down to a minimum of 1 instance.

This setup allows your web app to handle higher loads while keeping costs in check by scaling down during low-demand times. With autoscaling in place, your application will be better equipped to manage fluctuations in usage efficiently.

What are Azure Autoscaling Best Practices?

To make the most of Azure autoscaling, it’s important to configure settings wisely. Here are some best practices to ensure effective scaling for your applications.

1. Ensure an Adequate Margin Between Minimum and Maximum Values

When setting up Azure autoscaling, make sure to define minimum and maximum instance counts with enough space between them. If the minimum and maximum values are the same (e.g., both set to 2), autoscaling cannot take any action, as there’s no range to scale within. Set a reasonable margin, like a minimum of 2 and a maximum of 5, to allow the application to scale up or down effectively. Azure autoscaling will always scale within these limits to match the demand.

2. Understand the Impact of Manual Scaling

When you manually adjust the instance count, Azure autoscaling will reset it according to the configured minimum and maximum values. For instance, if you have set a range of 3 to 6 instances and manually increase the count to 8, autoscaling will reduce it to 6 in the next cycle. Similarly, if you set it below the minimum, it will scale back up. For consistency, always review and adjust your autoscaling rules instead of making temporary manual changes.

3. Always Use Both Scale-Out and Scale-In Rules

For effective resource management, use a combination of scale-out (increase instances) and scale-in (decrease instances) rules. If you set only one type, such as scaling out during high usage, Azure autoscaling will continue adding instances up to the maximum limit, without scaling down when demand drops. A scale-in rule helps save costs by reducing resources during low demand. Using both types ensures a balanced approach, maintaining performance and cost-effectiveness.

4. Use the Same Metric for Both Scale-Out and Scale-In Rules

When setting scale-out and scale-in rules, use the same metric to prevent “flapping,” where autoscaling frequently adds and removes instances. For example, if you use CPU usage as a metric, set both scale-out and scale-in rules based on it. Using different metrics (e.g., CPU for scale-out and memory for scale-in) can cause conflicts where both conditions are met at the same time, leading to frequent, unnecessary adjustments. Keeping a single metric ensures smoother, more predictable scaling.

5. Choose the Appropriate Metric Statistic

Azure autoscaling allows you to choose among Average, Minimum, Maximum, and Total statistics for each metric. The Average statistic is the most commonly used, as it provides a balanced view of resource usage over time. Selecting the right statistic depends on your workload and goals. For instance:

• Use Average for stable, predictable scaling.
• Use Maximum for scaling based on peak usage to ensure capacity during demand spikes.

How Economize Can Save You Money Beyond Autoscaling?

While autoscaling is effective in optimizing resources and managing costs, it does not cover all aspects of cloud cost optimization. Economize offers a comprehensive cloud cost management solution that goes beyond autoscaling to help businesses control expenses effectively. Here’s how:

• Real-Time Cost Monitoring: Economize provides real-time insights into cloud costs, helping you understand where your expenses are concentrated and identifying patterns that lead to unnecessary spending.
• Smart Cost-Saving Recommendations: With advanced analytics, Economize identifies potential savings opportunities, such as underutilized resources, unused storage, or redundant services. It offers actionable insights that allow teams to fine-tune resources even further.
• Auto-Tagging and Policy Enforcement: Ensuring resources are properly tagged can help track usage across teams or departments, making it easier to understand cost allocation. Economize automates tagging and enforces cost-saving policies, allowing organizations to streamline cost management.
• Budget Alerts and Anomaly Detection: Economize provides alerts when costs exceed predefined budgets and uses anomaly detection to catch unexpected spikes, giving businesses a proactive approach to managing expenses.
• Integration with Azure: Economize integrates seamlessly with Azure, making it a practical extension to your autoscaling setup by providing insights and recommendations specifically designed for Azure environments.

Conclusion

Effective Azure autoscaling requires careful planning and ongoing monitoring to ensure that scaling decisions match your application’s needs. By setting a reasonable range for instance limits, using both scale-out and scale-in rules, and choosing appropriate metrics, you can achieve smooth, efficient scaling. Testing, regular adjustments, and monitoring are also essential for optimal performance and cost control.

However, while autoscaling handles resource allocation effectively, comprehensive cloud cost management requires additional tools. Economize fills this gap by offering real-time cost insights, smart recommendations, and advanced tools for optimizing cloud expenses beyond the limits of autoscaling. Together, Azure Autoscaling and Economize provide a powerful framework for efficient cloud resource management

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