Scale servers

As your application load grows, the infrastructure resources it consumes also increase. Understanding how your application's resource usage patterns change over time is crucial for developing an effective scaling strategy that maintains performance while optimizing costs.

Autoscaling enables you to automatically add or remove server instances based on predefined metrics and thresholds. This approach ensures your application has sufficient capacity during peak demand while avoiding unnecessary costs during low-usage periods.

Implementing effective server scaling requires understanding your application's performance characteristics, selecting appropriate scaling metrics, and configuring your autoscaling policies to respond quickly to changing demand without causing instability.

Define scaling metrics and thresholds

Effective server scaling starts with identifying the right metrics to monitor and setting appropriate thresholds for scaling actions. Common metrics include CPU utilization, memory usage, network traffic, and application-specific indicators like response times or queue depths.

Monitor your application's baseline performance during normal operation to establish realistic scaling thresholds. Set your scale-up threshold high enough to avoid unnecessary scaling during temporary spikes, but low enough to prevent performance degradation. Similarly, set your scale-down threshold to avoid rapid scaling cycles while still reducing costs during low demand.

Use application-specific metrics when possible, as they provide more accurate indicators of actual user experience than infrastructure metrics alone. For example, if your application is database-intensive, monitor database connection pool utilization rather than just CPU usage.

Implement autoscaling groups

Autoscaling groups provide the foundation for automatic server scaling across different cloud providers. These groups manage a collection of server instances that can be automatically scaled based on your defined policies and metrics.

Configure your autoscaling group with appropriate minimum, desired, and maximum instance counts. The minimum count ensures baseline capacity for your application, while the maximum count prevents runaway scaling that could exhaust your budget or cloud provider limits.

Use Terraform to manage your autoscaling group configurations as code. This ensures consistent scaling policies across environments and enables version control for your infrastructure configurations. Define your autoscaling groups, launch templates, and scaling policies in Terraform rather than managing them manually.

Configure scaling policies

Scaling policies define how your autoscaling group responds to changing demand. Implement both scale-up and scale-down policies with appropriate cooldown periods to prevent rapid scaling cycles that could destabilize your application.

Set scale-up policies to add instances quickly when demand increases, but include cooldown periods to allow new instances to fully initialize before considering additional scaling actions. Configure scale-down policies to remove instances more conservatively, ensuring you do not remove capacity that is still needed.

Use step scaling policies for more granular control over scaling behavior. These policies allow you to define different scaling actions based on the magnitude of the metric breach, enabling more sophisticated scaling strategies that match your application's performance characteristics.

Monitor scaling performance

Comprehensive monitoring is essential for optimizing your server scaling strategy. Track scaling events, instance health, and application performance to ensure your autoscaling policies are working effectively.

Monitor key metrics like scaling frequency, scaling latency, and scaling efficiency to identify opportunities for optimization. High scaling frequency might indicate overly sensitive thresholds, while long scaling latency could suggest issues with instance provisioning or application startup times.

Configure alerts for scaling events to ensure you are aware of when and why your infrastructure is scaling. Use centralized logging to correlate scaling events with application performance and user experience metrics.

Next steps

In this section of Scale resources, you learned about implementing automatic server scaling, including defining scaling metrics, configuring autoscaling groups, implementing scaling policies, and monitoring scaling performance. Scale servers is part of the Optimize systems.

Refer to the following documents to learn more about server scaling:

• Scale containers to implement container-level scaling strategies
• Detect configuration drift to maintain consistent configurations across your infrastructure
• Identify common metrics to monitor the right performance indicators

If you are interested in learning more about server scaling and autoscaling, you can check out the following resources:

• AWS provider Auto Scaling Group resource - Terraform documentation for AWS autoscaling
• Manage AWS Auto Scaling Groups - Tutorial for implementing AWS autoscaling with Terraform
• Azure provider Virtual Machine Scale Set resource - Terraform documentation for Azure scaling
• Manage Azure Virtual Machine Scale Sets with Terraform - Tutorial for implementing Azure scaling with Terraform
• GCP provider Autoscaler resource - Terraform documentation for GCP autoscaling