AWS Auto Scaling

1. Introduction

AWS Auto Scaling is AWS’s unified scaling service that goes beyond merely managing Amazon EC2 instances. It provides a single, centralized way to monitor and automatically adjust the capacity of multiple AWS resources based on real‐time demand and performance targets. This service helps ensure your applications run at optimal performance while keeping costs in check.

2. What Is AWS Auto Scaling?

Unlike Amazon EC2 Auto Scaling—which is limited to scaling EC2 instances—AWS Auto Scaling (often referred to as Application Auto Scaling) lets you configure scaling for a wide variety of AWS services. These include:

• AppStream 2.0 fleets
• Aurora replicas: Scale database read capacity dynamically.
• Amazon Comprehend document classification and entity recognizer endpoints
• DynamoDB tables and global secondary indexes: Manage read and write capacity on tables and secondary indexes.
• Amazon ECS services: Automatically adjust the number of tasks in your containerized applications.
• ElastiCache (Redis OSS) clusters (replication groups)
• Amazon EMR clusters
• Amazon Keyspaces (for Apache Cassandra) tables
• Lambda function provisioned concurrency: Ensure your Lambda functions have the right number of pre-initialized instances.
• Amazon Managed Streaming for Apache Kafka (MSK) broker storage
• Amazon Neptune clusters
• SageMaker AI endpoint variants
• SageMaker AI inference components
• SageMaker AI Serverless provisioned concurrency
• Spot Fleet requests
• Pool of Amazon WorkSpaces

Furthermore, with a single, intuitive interface, you can set up scaling plans that define the minimum, maximum, and desired capacities for each resource type. This unified approach simplifies management across different AWS services.

3. Scaling Methods

AWS Auto Scaling offers several scaling methods that let you automatically adjust resource capacity based on demand: Target Tracking Scaling, Step Scaling, Scheduled Scaling, and Predictive Scaling. These scaling methods can be used individually or combined as part of a unified scaling plan to meet the specific needs of your applications.

The following table shows the supported scaling methods for each service:

Service	Target Tracking	Step Scaling	Predictive Scaling	Scheduled Scaling
AppStream 2.0 fleets	✅	✅	❌	✅
Aurora DB clusters	✅	✅	❌	✅
Amazon Comprehend (doc classification/entity endpoints)	✅	❌	❌	✅
DynamoDB tables/global secondary indexes	✅	❌	❌	✅
ECS services	✅	✅	✅	✅
ElastiCache (Redis OSS) replication groups	✅	❌	❌	✅
Amazon Keyspaces tables	✅	❌	❌	✅
AWS Lambda provisioned concurrency	✅	❌	❌	✅
Amazon MSK cluster storage (scale-out only)	✅	❌	❌	❌
Neptune clusters	✅	❌	❌	✅
SageMaker AI endpoints (variants, serverless, inference components)	✅	✅	❌	✅
Spot Fleets	✅	✅	❌	✅
WorkSpaces pools	✅	✅	❌	✅

3.1. Target Tracking Scaling

You define a specific target value for a chosen metric (for example, average CPU utilization or request count). AWS Auto Scaling continuously monitors this metric and adjusts capacity to maintain the target—similar to how a thermostat regulates room temperature.

• Operation:
  • If the metric goes above the target, the service scales out (adds resources) to bring the metric back down.
  • If the metric falls below the target, it scales in (removes resources) to avoid over-provisioning.
• Benefits:
  • Simplicity: You don’t need to specify complex thresholds for both scale-out and scale-in actions.
  • Balanced Performance: It provides steady performance by continuously keeping the metric near the desired target.
• Use Cases:
  • Web applications that need to maintain a steady average response time.
  • Any scenario where a single, key performance indicator reliably reflects load.

3.2. Step Scaling

Step scaling policies use a series of predefined “steps” to adjust capacity incrementally based on the degree of metric deviation.

• Operation:
  • You set up CloudWatch alarms that trigger when a metric (like CPU utilization) breaches defined thresholds.
  • Depending on how much the metric exceeds (or falls below) the threshold, different “step adjustments” are applied. For example, a slight breach might add one instance, while a larger deviation could add several.
• Benefits:
  • Granularity: It allows more controlled and graduated scaling responses.
  • Customization: Different step adjustments for scale-out and scale-in enable you to fine-tune how aggressively your application responds to load changes.
• Use Cases:
  • Applications with variable and sudden load spikes where a one-size-fits-all scaling action might be too coarse.
  • Scenarios where you want to ensure that your scaling actions are proportional to the severity of the load change.

3.3. Scheduled Scaling

Scheduled scaling lets you define scaling actions based on a pre-determined schedule. Instead of reacting to real-time metrics, capacity changes occur at specific times.

• Operation:
  • You configure scheduled actions (for example, scaling up every weekday morning and scaling down during off-peak hours).
  • These actions adjust the minimum, maximum, or desired capacity of your resource group on a recurring or one-time basis.
• Benefits:
  • Predictability: Ideal for workloads with predictable traffic patterns.
  • Proactive Adjustment: Prepares your system for known events (like daily peak traffic) before the load actually increases.
• Use Cases:
  • E-commerce sites with known daily or seasonal traffic peaks.
  • Batch processing systems that run at specific times.

3.4. Predictive Scaling

Predictive scaling uses machine learning and historical usage patterns to forecast future demand. It proactively scales capacity ahead of anticipated load increases.

• Operation:
  • AWS Auto Scaling analyzes historical CloudWatch metrics and current trends.
  • Based on these predictions, it automatically adjusts capacity in advance of expected demand surges.
• Benefits:
  • Reduced Latency: Helps avoid the delays inherent in reactive scaling, ensuring capacity is available right when needed.
  • Improved User Experience: By pre-scaling, applications are better prepared to handle sudden spikes in traffic without degradation in performance.
• Use Cases:
  • Environments where load spikes occur rapidly and without much warning.
  • Workloads with well-defined historical patterns that can be reliably forecast.

4. Benefits

• Optimized Performance and Cost:
  By dynamically adjusting capacity to match demand, AWS Auto Scaling helps maintain consistent application performance while minimizing over-provisioning (and thus unnecessary costs).

• Simplified Management:
  With a single interface to manage scaling across multiple services, you can avoid the complexity of configuring separate scaling policies for each resource type.

• Resilience and High Availability:
  The service’s ability to scale resources across multiple availability zones (and even multiple services) helps ensure that your applications remain available even during traffic spikes or resource disruptions.

• Proactive Scaling:
  Features like predictive scaling allow your infrastructure to be ready ahead of demand spikes, ensuring seamless user experiences during peak times.

5. Conclusion

AWS Auto Scaling is the centralized, unified service that not only scales EC2 instances (via Amazon EC2 Auto Scaling) but also automatically manages scaling for a range of other AWS resources. By integrating with CloudWatch, offering multiple scaling policy options, and enabling predictive as well as scheduled scaling, it helps you optimize performance, reduce costs, and simplify the management of dynamic cloud environments—all without additional service charges.

For further information, refer to the official AWS Auto Scaling webpage and the Application Auto Scaling User Guide available on AWS’s documentation site.