@aws-cdk/aws-autoscaling

Amazon EC2 Auto Scaling Construct Library

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This is a developer preview (public beta) module. Releases might lack important features and might have future breaking changes.

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This module is part of the AWS Cloud Development Kit project.

Fleet

Auto Scaling Group

An AutoScalingGroup represents a number of instances on which you run your code. You pick the size of the fleet, the instance type and the OS image:

import autoscaling = require('@aws-cdk/aws-autoscaling');
import ec2 = require('@aws-cdk/aws-ec2');

new autoscaling.AutoScalingGroup(this, 'ASG', {
    vpc,
    instanceType: ec2.InstanceType.of(InstanceClass.Burstable2, InstanceSize.Micro),
    machineImage: new ec2.AmazonLinuxImage() // get the latest Amazon Linux image
});

NOTE: AutoScalingGroup has an property called allowAllOutbound (allowing the instances to contact the internet) which is set to true by default. Be sure to set this to false if you don't want your instances to be able to start arbitrary connections.

Machine Images (AMIs)

AMIs control the OS that gets launched when you start your EC2 instance. The EC2 library contains constructs to select the AMI you want to use.

Depending on the type of AMI, you select it a different way.

The latest version of Amazon Linux and Microsoft Windows images are selectable by instantiating one of these classes:

example of creating images

NOTE: The Amazon Linux images selected will be cached in your cdk.json, so that your AutoScalingGroups don't automatically change out from under you when you're making unrelated changes. To update to the latest version of Amazon Linux, remove the cache entry from the context section of your cdk.json.

We will add command-line options to make this step easier in the future.

AutoScaling Instance Counts

AutoScalingGroups make it possible to raise and lower the number of instances in the group, in response to (or in advance of) changes in workload.

When you create your AutoScalingGroup, you specify a minCapacity and a maxCapacity. AutoScaling policies that respond to metrics will never go higher or lower than the indicated capacity (but scheduled scaling actions might, see below).

There are three ways to scale your capacity:

• In response to a metric (also known as step scaling); for example, you might want to scale out if the CPU usage across your cluster starts to rise, and scale in when it drops again.
• By trying to keep a certain metric around a given value (also known as target tracking scaling); you might want to automatically scale out and in to keep your CPU usage around 50%.
• On a schedule; you might want to organize your scaling around traffic flows you expect, by scaling out in the morning and scaling in in the evening.

The general pattern of autoscaling will look like this:

const autoScalingGroup = new autoscaling.AutoScalingGroup(this, 'ASG', {
  minCapacity: 5,
  maxCapacity: 100
  // ...
});

// Step scaling
autoScalingGroup.scaleOnMetric(...);

// Target tracking scaling
autoScalingGroup.scaleOnCpuUtilization(...);
autoScalingGroup.scaleOnIncomingBytes(...);
autoScalingGroup.scaleOnOutgoingBytes(...);
autoScalingGroup.scaleOnRequestCount(...);
autoScalingGroup.scaleToTrackMetric(...);

// Scheduled scaling
autoScalingGroup.scaleOnSchedule(...);

Step Scaling

This type of scaling scales in and out in deterministics steps that you configure, in response to metric values. For example, your scaling strategy to scale in response to a metric that represents your average worker pool usage might look like this:

 Scaling        -1          (no change)          +1       +3
            │        │                       │        │        │
            ├────────┼───────────────────────┼────────┼────────┤
            │        │                       │        │        │
Worker use  0%      10%                     50%       70%     100%

(Note that this is not necessarily a recommended scaling strategy, but it's a possible one. You will have to determine what thresholds are right for you).

Note that in order to set up this scaling strategy, you will have to emit a metric representing your worker utilization from your instances. After that, you would configure the scaling something like this:

const workerUtilizationMetric = new cloudwatch.Metric({
    namespace: 'MyService',
    metricName: 'WorkerUtilization'
});

capacity.scaleOnMetric('ScaleToCPU', {
  metric: workerUtilizationMetric,
  scalingSteps: [
    { upper: 10, change: -1 },
    { lower: 50, change: +1 },
    { lower: 70, change: +3 },
  ],

  // Change this to AdjustmentType.PercentChangeInCapacity to interpret the
  // 'change' numbers before as percentages instead of capacity counts.
  adjustmentType: autoscaling.AdjustmentType.ChangeInCapacity,
});

The AutoScaling construct library will create the required CloudWatch alarms and AutoScaling policies for you.

Target Tracking Scaling

This type of scaling scales in and out in order to keep a metric around a value you prefer. There are four types of predefined metrics you can track, or you can choose to track a custom metric. If you do choose to track a custom metric, be aware that the metric has to represent instance utilization in some way (AutoScaling will scale out if the metric is higher than the target, and scale in if the metric is lower than the target).

If you configure multiple target tracking policies, AutoScaling will use the one that yields the highest capacity.

The following example scales to keep the CPU usage of your instances around 50% utilization:

autoScalingGroup.scaleOnCpuUtilization('KeepSpareCPU', {
  targetUtilizationPercent: 50
});

To scale on average network traffic in and out of your instances:

autoScalingGroup.scaleOnIncomingBytes('LimitIngressPerInstance', {
    targetBytesPerSecond: 10 * 1024 * 1024 // 10 MB/s
});
autoScalingGroup.scaleOnOutcomingBytes('LimitEgressPerInstance', {
    targetBytesPerSecond: 10 * 1024 * 1024 // 10 MB/s
});

To scale on the average request count per instance (only works for AutoScalingGroups that have been attached to Application Load Balancers):

autoScalingGroup.scaleOnRequestCount('LimitRPS', {
    targetRequestsPerSecond: 1000
});

Scheduled Scaling

This type of scaling is used to change capacities based on time. It works by changing minCapacity, maxCapacity and desiredCapacity of the AutoScalingGroup, and so can be used for two purposes:

• Scale in and out on a schedule by setting the minCapacity high or the maxCapacity low.
• Still allow the regular scaling actions to do their job, but restrict the range they can scale over (by setting both minCapacity and maxCapacity but changing their range over time).

A schedule is expressed as a cron expression. There is a Cron helper class to help build cron expressions.

The following example scales the fleet out in the morning, going back to natural scaling (all the way down to 1 instance if necessary) at night:

autoScalingGroup.scaleOnSchedule('PrescaleInTheMorning', {
  schedule: autoscaling.Cron.dailyUtc(8),
  minCapacity: 20,
});

autoScalingGroup.scaleOnSchedule('AllowDownscalingAtNight', {
  schedule: autoscaling.Cron.dailyUtc(20),
  minCapacity: 1
});

Allowing Connections

See the documentation of the @aws-cdk/aws-ec2 package for more information about allowing connections between resources backed by instances.

Future work

• CloudWatch Events (impossible to add currently as the AutoScalingGroup ARN is necessary to make this rule and this cannot be accessed from CloudFormation).

Changelog

0.39.0 (2019-07-08)

Bug Fixes

• codepipeline: mark crossRegionReplicationBuckets and crossRegionSupport as experimental. (#3226) (f8256e7)
• assets: packages assets, aws-ecr-assets and aws-s3-assets are now experimental instead of stable

BREAKING CHANGES TO EXPERIMENTAL FEATURES

• codepipeline: Pipeline.crossRegionReplicationBuckets is now experimental
• codepipeline: Pipeline.crossRegionSupport is now experimental
• codepipeline: CrossRegionSupport is now experimental
• assets: package assetsis now experimental instead of stable
• aws-ecr-assets: package aws-ecr-assetsis now experimental instead of stable
• aws-s3-assets: package aws-s3-assetsis now experimental instead of stable