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Autoscaling on AWS 
Dhruv Parpia 
Solutions Architect, ASEAN
v 
Topics We’ll Cover 
• Amazon EC2 
• Types of Scaling 
• Auto Scaling Introduction 
• Auto Scaling Terminology 
• Best Practices 
• Console Demo
Amazon Elastic Compute Cloud (EC2) Amazon EC2 
v 
Basic unit of compute capacity 
Range of CPU, memory & local disk options 
Feature Details 
Flexible Run windows or linux distributions 
Scalable Wide range of instance types from micro to cluster compute 
Machine Images Configurations can be saved as machine images (AMIs) from which new instances can be 
created 
Full control Full root or administrator rights 
Secure Full firewall control via Security Groups 
Monitoring Publishes metrics to Cloud Watch 
Inexpensive On-demand, Reserved and Spot instance types 
VM Import/Export Import and export VM images to transfer configurations in and out of EC2
v 
Types of Scaling 
• Vertical Scaling 
• Changing instance size 
• Increasing EBS Capacity 
• Horizontal Scaling 
• Adding / removing instances 
• ELB 
m3.medium m3.medium m3.medium 
r3.8xlarge 
m3.medium m3.medium m3.medium 
c3.2xlarge 
m3.medium
“We’re gonna need a bigger box” 
v 
• Different EC2 instance type 
• High memory instances 
• High CPU instances 
• High I/O instances 
• High storage instances 
• Can now leverage PIOPs 
• Easy to change instance sizes 
• Will hit an endpoint eventually 
r3.8xlarge 
c3.2xlarge 
m3.medium
“We’re gonna need a bigger box” 
v 
• Different EC2 instance type 
• High memory instances 
• High CPU instances 
• High I/O instances 
• High storage instances 
• Can now leverage PIOPs 
• Easy to change instance sizes 
• Will hit an endpoint eventually 
r3.8xlarge 
c3.2xlarge 
m3.medium
Traditional IT Usage Patterns 
v 
On and Off Fast Growth 
Variable peaks Predictable peaks
Traditional IT Usage Patterns 
v 
On and Off Fast Growth 
WASTE 
Poor 
Service 
Variable peaks Predictable peaks
v 
Auto Scaling 
• Automatic resizing of compute clusters based on demand 
• Define minimum and maximum number of instances 
• Define when scaling out and in occurs 
• Use metrics collected in Amazon CloudWatch to drive scaling 
• Run Auto Scaling for On-Demand and Spot instance types 
• Its Free! Amazon 
CloudWatch 
Usage 
Metrics 
Scaling 
Instructions 
Auto Scaling Group 
Queue 
Metrics 
Auto Scaling
Cloud IT Usage Patterns (Auto Scaling) 
v 
On and Off Fast Growth 
Variable peaks Predictable peaks
Terminology for Auto Scaling 
v 
• Auto Scaling Group 
• Launch Configuration 
• Scaling Plan 
• Amazon CloudWatch Alarm 
• Amazon SNS Notification 
• Elastic Load Balancer 
• Instance Lifecycle 
• Instance Metadata / Userdata
v 
Auto Scaling and AWS 
Auto Scaling Groups 
• EC2 instances are categorized 
into Auto Scaling groups. 
• Create Auto Scaling groups by 
defining the minimum, 
maximum, and, optionally, the 
desired number of running 
EC2 instances. 
Launch Configuration 
• Auto Scaling groups use a 
launch configuration to 
launch EC2 instances. 
• Create the launch 
configuration by providing 
information about the image 
you want Auto Scaling to use 
to launch EC2 instances 
Scaling Plan 
• A scaling plan tells Auto 
Scaling when and how to 
scale. 
• Create a scaling plan based on 
the occurrence of specified 
conditions (dynamic scaling) 
or create a plan based on a 
specific schedule.
Retrieving Resource Metrics 
v 
CloudWatch 
• CloudWatch: A web service that enables you to monitor and manage 
various metrics, and configure alarm actions based on data from those 
metrics. 
• A CloudWatch alarm is an object that monitors a single metric over a 
specific period. 
• A metric is a variable that you want to monitor. eg: CPU usage, or incoming 
network traffic. 
• The alarm changes its state when the value of the metric breaches a 
defined range and maintains the change for a specified number of 
periods.
Planning your Auto Scaling Group 
v 
• How long it takes to launch and configure a server 
• What metrics have the most relevance to your application's 
performance 
• What existing resources (such as EC2 instances or AMIs) you might 
want to use as part of your Auto Scaling group 
• How many Availability Zones you want to the Auto Scaling group to 
span 
• The role you want Auto Scaling to play in your application.
Load Balance your Auto Scaling Group 
v 
Elastic Load Balancing 
• Distribute incoming web traffic automatically. 
• Single point of entry for your application. 
• Sends data about your load balancers and EC2 instances to Amazon 
CloudWatch. 
• Use Elastic Load Balancing metrics to scale your application. 
• Use connection draining to wait for the in-flight requests to complete.
Understanding Auto Scaling Cooldowns 
v 
Auto Scaling group 
alarm 
Auto Scaling 
New instance 
EC2 instances
Understanding Auto Scaling Cooldowns 
v 
Default 
Cooldown 
in effect? 
Launch or 
terminate instance 
Scaling action 
occurs 
Ignore action 
No 
Yes
v 
Auto Scaling Lifecycles
Auto Scaling Lifecycle Hooks 
v
Introducing Bootstrapping 
v 
Bootstrapping: some examples 
• Install latest software 
• Copy data from S3 
• Register with DNS 
• Start services 
• Update packages 
• Reboot 
• Open port 80 
• Register with load balancer 
• Mount devices
v 
Bootstrapping tools 
• Scripts on instance (Bash, Powershell) 
• ConfigManagement Tools (Chef, Puppet) 
• Amazon OpsWorks
EC2 Metadata and UserData 
v 
• Every EC2 Instance has access to local instance metadata and userdata service 
• Metadata: immutable information about the instance 
Accessible from within the instance via HTTP at 
• http://169.254.169.254/latest/meta-data/
EC2 Metadata and UserData 
v 
EC2 Metadata and UserData 
Script(s) on instance may retrieve useful information about the instance, such as: 
• Host name 
• AMI ID 
• Instance ID 
• Public/Private DNS 
• Availability Zone
EC2 Metadata and UserData 
v 
EC2 Metadata and UserData 
• Pass up to 16KB of text to an instance on launch 
• Text can be parsed by script on instance and used to configure the machine
UserData and CloudInit 
v 
• CloudInit executes UserData on first boot if UserData begins with: 
• #! (Linux) 
• <script> (Windows; technically, EC2Config, not CloudInit, does this)
UserData and CloudInit 
v 
• CloudInit executes UserData on first boot if UserData begins with: 
• #! (Linux) 
• <script> (Windows; technically, EC2Config, not CloudInit, does this) 
• CloudInit is installed on Amazon Linux, Ubuntu, and RHEL AMIs 
• EC2Config is installed on Windows Server AMIs 
• Both may be installed on other distributions via a package repo or source
UserData and CloudInit 
v 
• UserData to install Apache and MySQL on boot, and attach an EIP: 
#!/bin/bash 
# Install Apache, PHP, and MySQL 
yum install –y httpd mysql-server 
# Attach an Elastic IP to this instance 
ec2-associate-address  
23.34.45.56  
-i $(curl http://169.254.169.254/latest/meta-data/instance-id)
v 
Instance 
request 
User 
data
v 
Instance 
request 
User 
data 
Meta-data 
service
v 
Instance 
request 
User 
data 
Meta-data 
service 
Instance
v 
31 
Amazon Windows EC2Config Service executes user-data on 
launch: 
<script>dir > c:test.log</script> 
<powershell>any command that you can run</powershell> 
AWS Powershell Tools (use IAM roles as before…) 
<powershell> 
Read-S3Object -BucketName myS3Bucket 
-Key myFolder/myFile.zip 
-File c:destinationFile.zip 
</powershell>
Automation 
Less fingers, less mistakes 
v 
Why do this? 
Availability 
Drive higher 
availability with self-healing 
Security 
Instances locked 
down by default 
Flexible 
Shell, Powershell, 
CloudFormation,C 
hef, Puppet, 
OpsWorks 
Scale 
Manage large scale 
deployments and drive 
autoscaling 
Efficiency 
Audit and manage 
your estate with less 
time & effort
v 
Some Do’s and Don’ts 
Do 
Use IAM roles 
Go keyless if you can 
Strike a balance between 
AMI and dynamic 
bootstrapping
v 
Do Don’t 
Use IAM roles 
Go keyless if you can 
Strike a balance between 
AMI and dynamic 
bootstrapping 
Put your API access keys 
into code (and then publish 
to GIT) or bake into AMIs 
(and share) 
 
Some Do’s and Don’ts
v 
Scaling Demo
Autoscaling isn’t one size fits all 
v 
• Choose the right metrics 
• CPU Usage 
• Queue Depth 
• Number of concurrent users 
• Scale too aggressively 
• Overprovisioning: increases costs 
• Bounciness: Add more than you need and have to partially scale back shortly after 
scaling up, increasing costs. 
• Scale too timidly 
• Poor performance 
• Outages due to lack of capacity 
• Scale out early / Scale in slowly
v 
What’s new? 
• Attach / Detach Instances from Auto Scaling Groups 
• Place instances into Standby State to Troubleshoot 
• Hold instances in Pending state for installing software / retrieve logs 
• Create an Auto Scaling Group / Launch Configuration based on a 
running instance

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Day 5 - AWS Autoscaling Master Class - The New Capacity Plan

  • 1. Autoscaling on AWS Dhruv Parpia Solutions Architect, ASEAN
  • 2. v Topics We’ll Cover • Amazon EC2 • Types of Scaling • Auto Scaling Introduction • Auto Scaling Terminology • Best Practices • Console Demo
  • 3. Amazon Elastic Compute Cloud (EC2) Amazon EC2 v Basic unit of compute capacity Range of CPU, memory & local disk options Feature Details Flexible Run windows or linux distributions Scalable Wide range of instance types from micro to cluster compute Machine Images Configurations can be saved as machine images (AMIs) from which new instances can be created Full control Full root or administrator rights Secure Full firewall control via Security Groups Monitoring Publishes metrics to Cloud Watch Inexpensive On-demand, Reserved and Spot instance types VM Import/Export Import and export VM images to transfer configurations in and out of EC2
  • 4. v Types of Scaling • Vertical Scaling • Changing instance size • Increasing EBS Capacity • Horizontal Scaling • Adding / removing instances • ELB m3.medium m3.medium m3.medium r3.8xlarge m3.medium m3.medium m3.medium c3.2xlarge m3.medium
  • 5. “We’re gonna need a bigger box” v • Different EC2 instance type • High memory instances • High CPU instances • High I/O instances • High storage instances • Can now leverage PIOPs • Easy to change instance sizes • Will hit an endpoint eventually r3.8xlarge c3.2xlarge m3.medium
  • 6. “We’re gonna need a bigger box” v • Different EC2 instance type • High memory instances • High CPU instances • High I/O instances • High storage instances • Can now leverage PIOPs • Easy to change instance sizes • Will hit an endpoint eventually r3.8xlarge c3.2xlarge m3.medium
  • 7. Traditional IT Usage Patterns v On and Off Fast Growth Variable peaks Predictable peaks
  • 8. Traditional IT Usage Patterns v On and Off Fast Growth WASTE Poor Service Variable peaks Predictable peaks
  • 9. v Auto Scaling • Automatic resizing of compute clusters based on demand • Define minimum and maximum number of instances • Define when scaling out and in occurs • Use metrics collected in Amazon CloudWatch to drive scaling • Run Auto Scaling for On-Demand and Spot instance types • Its Free! Amazon CloudWatch Usage Metrics Scaling Instructions Auto Scaling Group Queue Metrics Auto Scaling
  • 10. Cloud IT Usage Patterns (Auto Scaling) v On and Off Fast Growth Variable peaks Predictable peaks
  • 11. Terminology for Auto Scaling v • Auto Scaling Group • Launch Configuration • Scaling Plan • Amazon CloudWatch Alarm • Amazon SNS Notification • Elastic Load Balancer • Instance Lifecycle • Instance Metadata / Userdata
  • 12. v Auto Scaling and AWS Auto Scaling Groups • EC2 instances are categorized into Auto Scaling groups. • Create Auto Scaling groups by defining the minimum, maximum, and, optionally, the desired number of running EC2 instances. Launch Configuration • Auto Scaling groups use a launch configuration to launch EC2 instances. • Create the launch configuration by providing information about the image you want Auto Scaling to use to launch EC2 instances Scaling Plan • A scaling plan tells Auto Scaling when and how to scale. • Create a scaling plan based on the occurrence of specified conditions (dynamic scaling) or create a plan based on a specific schedule.
  • 13. Retrieving Resource Metrics v CloudWatch • CloudWatch: A web service that enables you to monitor and manage various metrics, and configure alarm actions based on data from those metrics. • A CloudWatch alarm is an object that monitors a single metric over a specific period. • A metric is a variable that you want to monitor. eg: CPU usage, or incoming network traffic. • The alarm changes its state when the value of the metric breaches a defined range and maintains the change for a specified number of periods.
  • 14. Planning your Auto Scaling Group v • How long it takes to launch and configure a server • What metrics have the most relevance to your application's performance • What existing resources (such as EC2 instances or AMIs) you might want to use as part of your Auto Scaling group • How many Availability Zones you want to the Auto Scaling group to span • The role you want Auto Scaling to play in your application.
  • 15. Load Balance your Auto Scaling Group v Elastic Load Balancing • Distribute incoming web traffic automatically. • Single point of entry for your application. • Sends data about your load balancers and EC2 instances to Amazon CloudWatch. • Use Elastic Load Balancing metrics to scale your application. • Use connection draining to wait for the in-flight requests to complete.
  • 16. Understanding Auto Scaling Cooldowns v Auto Scaling group alarm Auto Scaling New instance EC2 instances
  • 17. Understanding Auto Scaling Cooldowns v Default Cooldown in effect? Launch or terminate instance Scaling action occurs Ignore action No Yes
  • 18. v Auto Scaling Lifecycles
  • 20. Introducing Bootstrapping v Bootstrapping: some examples • Install latest software • Copy data from S3 • Register with DNS • Start services • Update packages • Reboot • Open port 80 • Register with load balancer • Mount devices
  • 21. v Bootstrapping tools • Scripts on instance (Bash, Powershell) • ConfigManagement Tools (Chef, Puppet) • Amazon OpsWorks
  • 22. EC2 Metadata and UserData v • Every EC2 Instance has access to local instance metadata and userdata service • Metadata: immutable information about the instance Accessible from within the instance via HTTP at • http://169.254.169.254/latest/meta-data/
  • 23. EC2 Metadata and UserData v EC2 Metadata and UserData Script(s) on instance may retrieve useful information about the instance, such as: • Host name • AMI ID • Instance ID • Public/Private DNS • Availability Zone
  • 24. EC2 Metadata and UserData v EC2 Metadata and UserData • Pass up to 16KB of text to an instance on launch • Text can be parsed by script on instance and used to configure the machine
  • 25. UserData and CloudInit v • CloudInit executes UserData on first boot if UserData begins with: • #! (Linux) • <script> (Windows; technically, EC2Config, not CloudInit, does this)
  • 26. UserData and CloudInit v • CloudInit executes UserData on first boot if UserData begins with: • #! (Linux) • <script> (Windows; technically, EC2Config, not CloudInit, does this) • CloudInit is installed on Amazon Linux, Ubuntu, and RHEL AMIs • EC2Config is installed on Windows Server AMIs • Both may be installed on other distributions via a package repo or source
  • 27. UserData and CloudInit v • UserData to install Apache and MySQL on boot, and attach an EIP: #!/bin/bash # Install Apache, PHP, and MySQL yum install –y httpd mysql-server # Attach an Elastic IP to this instance ec2-associate-address 23.34.45.56 -i $(curl http://169.254.169.254/latest/meta-data/instance-id)
  • 28. v Instance request User data
  • 29. v Instance request User data Meta-data service
  • 30. v Instance request User data Meta-data service Instance
  • 31. v 31 Amazon Windows EC2Config Service executes user-data on launch: <script>dir > c:test.log</script> <powershell>any command that you can run</powershell> AWS Powershell Tools (use IAM roles as before…) <powershell> Read-S3Object -BucketName myS3Bucket -Key myFolder/myFile.zip -File c:destinationFile.zip </powershell>
  • 32. Automation Less fingers, less mistakes v Why do this? Availability Drive higher availability with self-healing Security Instances locked down by default Flexible Shell, Powershell, CloudFormation,C hef, Puppet, OpsWorks Scale Manage large scale deployments and drive autoscaling Efficiency Audit and manage your estate with less time & effort
  • 33. v Some Do’s and Don’ts Do Use IAM roles Go keyless if you can Strike a balance between AMI and dynamic bootstrapping
  • 34. v Do Don’t Use IAM roles Go keyless if you can Strike a balance between AMI and dynamic bootstrapping Put your API access keys into code (and then publish to GIT) or bake into AMIs (and share)  Some Do’s and Don’ts
  • 36. Autoscaling isn’t one size fits all v • Choose the right metrics • CPU Usage • Queue Depth • Number of concurrent users • Scale too aggressively • Overprovisioning: increases costs • Bounciness: Add more than you need and have to partially scale back shortly after scaling up, increasing costs. • Scale too timidly • Poor performance • Outages due to lack of capacity • Scale out early / Scale in slowly
  • 37. v What’s new? • Attach / Detach Instances from Auto Scaling Groups • Place instances into Standby State to Troubleshoot • Hold instances in Pending state for installing software / retrieve logs • Create an Auto Scaling Group / Launch Configuration based on a running instance

Editor's Notes

  1. Scaling the one EC2 instance we have to a larger one is the most simple approach to start with. There are a lot of different AWS instance types to go with depending on your work load. Some have high I/O, CPU, Memory, or local storage. You can also make use of EBS-Optimized instances and Provisioned IOPs to help scale the storage for this instance quite a bit.
  2. Scaling the one EC2 instance we have to a larger one is the most simple approach to start with. There are a lot of different AWS instance types to go with depending on your work load. Some have high I/O, CPU, Memory, or local storage. You can also make use of EBS-Optimized instances and Provisioned IOPs to help scale the storage for this instance quite a bit.
  3. Talk about auto-scaling.
  4. Do you want Auto Scaling to use scaling to increase or decrease capacity? Or do you want to use it solely to ensure that a specific number of servers are always running? (Keep in mind that an Auto Scaling group can actually perform both functions simultaneously.)
  5. Consider the following scenario: you have a web application running in AWS. To make sure that the application always has the resources it needs to meet traffic demands, you create an Auto Scaling group. To help make sure the Auto Scaling group for the application tier has the appropriate amount of resources available, you create an CloudWatch alarm to occur whenever the CPUUtilization metric for the EC2 instances exceeds 90%. When the alarm occurs, Auto Scaling launches and configures another instance to join the application tier. These instances use a configuration script to install and configure software before the instance is put into service. As a result, it takes around two or three minutes from the time the instance launches to when it is in service. Now a spike in traffic occurs, causing the CloudWatch alarm to fire. When it does, Auto Scaling launches an instance to help handle the increase in demand. However, there’s a problem: the instance takes a couple of minutes to launch. During that time, the CloudWatch alarm could continue to fire, resulting in Auto Scaling launch another instance each time the alarm goes off. This is where the cooldown period comes into effect. With a cooldown period in place, Auto Scaling launches an instance and then suspends any scaling activities until a specific amount of time elapses. (The default amount of time is 300 seconds.) This way, the newly-launched instance has time to start handling application traffic. After the cooldown period expires, scaling actions resume for the Auto Scaling group. If the CloudWatch alarm is still occurring, Auto Scaling launches another instance, and the cooldown period takes effect again. If, however, the additional instance was enough to bring the CPU utilization back down, then the group remains at its current size. Default Cooldowns As illustrated in the previous example, an Auto Scaling cooldown period helps to ensure you don’t launch or terminate more resources than your application needs. Auto Scaling supports two types of cooldown periods: a default cooldown period and a scaling-specific cooldown period.
  6. Bootstrapping actions can be almost anything. Here are some examples.
  7. With AWS, you have a several tools to help you bootstrap instances. These include (but aren’t necessarily limited to) bash and PowerShell scripts, as well as configuration tools like Chef and Puppet.
  8. To maximize your use of bootstrapping with AWS, you should know how to access the MetaData and UserData of the EC2 instance.
  9. Your scripts can use this Metadata to get a lot useful information about the instance. The Metadata server also stores IAM role credentials.
  10. An EC2 instance can also include UserData. This are instructions that a script on the instance can parse and use to configure the system. Note that UserData has a maximize size of 16KB.
  11. So, I mentioned that there needs to be a script on the EC2 instance that can parse the UserData. Most EC2 instances include CloudInit, which can parse your UserData and run its instructions. To use CloudInit on Linux machines, start your UserData with #!. To use CloudInit on Windows machines, use <script>. (Technically, EC2Config parses these instructions, but the result is the same.)
  12. I said that most EC2 instances use CloudInit (or EC2Config). Let’s get more specific: CloudInit is available on Amazon Linux, Ubuntu, and RHEL Amazon Machine Images (AMIs). EC2Config is on all Windows Server AMIs. Either one may be available on other distributions, however.
  13. Here’s an example of UserData that: Installs Apache Installs MySQL Attaches an Amazon Elastic IP address (EIP)
  14. You now have more control over what happens to the Amazon EC2 instances running in your Auto Scaling groups at each stage of their lifecycle, from launch through termination. This makes it easier to update and run software on your EC2 instances, and troubleshoot and fix problems. Starting today, you can use new instance lifecycle action APIs to hold your newly launched instances in a pending state while you perform actions on them, such as installing software, before they are added to your Auto Scaling group and become in service. Similarly, when instances are terminating, you can temporarily stop the termination to investigate the cause or retrieve logs from an instance before it terminates. While instances are running in your Auto Scaling group, you can use the new DetachInstances action, and manage them just like instances that are not members of an Auto Scaling group. Combined with the AttachInstances action, you can easily move instances into and out of groups. You can also place instances into the new Standby state to troubleshoot or perform maintenance on them, then put them back into service once you are finished. When you place an instance in Standby, it will no longer receive new inbound connections from Elastic Load Balancers or count toward your group’s capacity until you put it back into service again. You can access these new features using the Auto Scaling API and command-line tools. They will be available in the AWS SDKs soon. To learn more about Auto Scaling, please visit the Auto Scaling Developer Guide.