---
title: Auto-Scaling Strategies and Policies
description: A comprehensive technical guide on auto-scaling strategies, including target tracking, step scaling, predictive scaling, and scaling policies with cooldown periods.
keywords: 
  - auto-scaling
  - scaling strategies
  - target tracking
  - step scaling
  - predictive scaling
  - scaling policies
  - cooldown periods
category: DevOps
tags:
  - auto-scaling
  - scaling
  - cloud computing
  - infrastructure
  - performance
  - resource management
---

## Auto-Scaling Strategies

Auto-scaling is the ability of a system to automatically adjust its resource capacity based on actual load. There are several strategies for implementing auto-scaling, each with its own advantages and use cases.

### Target Tracking Scaling

Target tracking scaling adjusts the capacity of an auto-scaling group to maintain a target value for a specific metric, such as average CPU utilization. The target tracking scaling policy attempts to scale out to handle increased load and scale in when the load decreases, keeping the target metric as close to the specified value as possible.

Example target tracking scaling policy:

```yaml
{
  "Type": "TargetTrackingScaling",
  "MetricSpecification": {
    "MetricName": "CPUUtilization",
    "Statistic": "Average",
    "TargetValue": 50.0
  },
  "ScaleInCooldown": 300,
  "ScaleOutCooldown": 60
}
```

In this example, the auto-scaling group will scale in and out to maintain an average CPU utilization of 50%. The `ScaleInCooldown` and `ScaleOutCooldown` properties specify the number of seconds to wait after a scaling activity before allowing another scaling activity.

### Step Scaling

Step scaling adjusts the capacity of an auto-scaling group based on a set of scaling adjustments, known as step adjustments. Each step adjustment specifies a metric value range and the corresponding adjustment to make to the current capacity, either an absolute number of instances or a percentage change.

Example step scaling policy:

```yaml
{
  "Type": "StepScaling",
  "AdjustmentType": "PercentChangeInCapacity",
  "MetricAggregationType": "Average",
  "Cooldown": 300,
  "StepAdjustments": [
    {
      "MetricIntervalLowerBound": 0,
      "MetricIntervalUpperBound": 60,
      "ScalingAdjustment": 25
    },
    {
      "MetricIntervalLowerBound": 60,
      "MetricIntervalUpperBound": 100,
      "ScalingAdjustment": 50
    },
    {
      "MetricIntervalLowerBound": 100,
      "ScalingAdjustment": 100
    }
  ]
}
```

In this example, the auto-scaling group will scale out by 25% if the average metric value is between 0 and 60, 50% if the average is between 60 and 100, and 100% if the average is above 100. The `Cooldown` property specifies the number of seconds to wait after a scaling activity before allowing another scaling activity.

### Predictive Scaling

Predictive scaling uses load forecasting to automatically scale the capacity of an auto-scaling group ahead of predicted traffic changes. It analyzes historical usage data and creates a scaling plan to meet future demand, adjusting the capacity of the auto-scaling group in advance.

Example predictive scaling configuration:

```yaml
{
  "MetricSpecification": {
    "MetricName": "RequestCount",
    "Namespace": "AWS/ApplicationELB",
    "Dimensions": [
      {
        "Name": "LoadBalancer",
        "Value": "my-load-balancer"
      }
    ],
    "Statistic": "Sum"
  },
  "Mode": "Forecast",
  "ForecastCapacityBuffer": 20,
  "ForecastLookbackPeriod": 86400,
  "ForecastingMode": "ForecastAndScale",
  "MaxCapacity": 100,
  "MinCapacity": 2,
  "PredefinedLoadMetricSpecification": {
    "PredefinedMetricType": "ASGTotalRequestCount"
  }
}
```

In this example, the predictive scaling configuration uses the `RequestCount` metric from the AWS/ApplicationELB namespace to forecast future load and automatically scale the auto-scaling group accordingly. The `ForecastCapacityBuffer` property specifies an additional percentage of capacity to provision beyond the forecasted demand, and the `ForecastLookbackPeriod` property specifies the historical data period to use for the forecast.

## Scaling Policies

Scaling policies define the specific rules and conditions that trigger auto-scaling actions. These policies can be based on target tracking, step scaling, or predictive scaling strategies.

### Simple Scaling Policy

A simple scaling policy adjusts the capacity of an auto-scaling group by a specific amount when a certain metric condition is met.

Example simple scaling policy:

```yaml
{
  "Type": "SimpleScaling",
  "AdjustmentType": "ChangeInCapacity",
  "ScalingAdjustment": 1,
  "Cooldown": 300,
  "MetricName": "CPUUtilization",
  "Namespace": "AWS/EC2",
  "Statistic": "Average",
  "ComparisonOperator": "GreaterThanThreshold",
  "Threshold": 80
}
```

In this example, the auto-scaling group will scale out by 1 instance when the average CPU utilization exceeds 80%. The `Cooldown` property specifies the number of seconds to wait after a scaling activity before allowing another scaling activity.

### Scheduled Scaling Policy

A scheduled scaling policy adjusts the capacity of an auto-scaling group based on a predetermined schedule, such as time of day or day of the week.

Example scheduled scaling policy:

```yaml
{
  "Type": "ScheduledAction",
  "Recurrence": "0 9 * * MON-FRI",
  "MinSize": 5,
  "MaxSize": 10,
  "DesiredCapacity": 8
}
```

In this example, the auto-scaling group will scale to a minimum of 5 instances, a maximum of 10 instances, and a desired capacity of 8 instances every weekday at 9 AM.

### Scaling Policy Cooldown Periods

Cooldown periods are a way to prevent rapid, consecutive scaling actions. They specify the amount of time to wait after a scaling activity before allowing another scaling activity.

Example cooldown period configuration:

```yaml
{
  "ScaleInCooldown": 300,
  "ScaleOutCooldown": 60
}
```

In this example, the auto-scaling group will wait 300 seconds (5 minutes) before allowing another scale-in activity, and 60 seconds (1 minute) before allowing another scale-out activity.

## Combining Scaling Strategies and Policies

Auto-scaling strategies and policies can be combined to create more complex and sophisticated auto-scaling solutions. For example, you could use a target tracking scaling policy to maintain a target CPU utilization, and then supplement it with a scheduled scaling policy to handle predictable load changes.

Example combined scaling configuration:

```yaml
{
  "TargetTrackingScaling": {
    "MetricSpecification": {
      "MetricName": "CPUUtilization",
      "Statistic": "Average",
      "TargetValue": 50.0
    },
    "ScaleInCooldown": 300,
    "ScaleOutCooldown": 60
  },
  "ScheduledActions": [
    {
      "Recurrence": "0 9 * * MON-FRI",
      "MinSize": 5,
      "MaxSize": 10,
      "DesiredCapacity": 8
    }
  ]
}
```

In this example, the auto-scaling group will use a target tracking scaling policy to maintain a 50% average CPU utilization, and a scheduled scaling policy to scale to a minimum of 5 instances, a maximum of 10 instances, and a desired capacity of 8 instances every weekday at 9 AM.

By combining different auto-scaling strategies and policies, you can create a more robust and adaptive auto-scaling solution that can handle a wide range of load patterns and requirements.