AWS re:Invent 2016: Scaling Your Web Applications with AWS Elastic Beanstalk (DEV206)
Leveraging Elastic Web-Scale Computing with AWS
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Transcript of Leveraging Elastic Web-Scale Computing with AWS
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Leveraging Elas-c Web-‐Scale Compu-ng with AWS Shiva Narayanaswamy
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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EC2 Basics
Virtual Servers in the Cloud • One instance to thousands of instances • In any public AWS region • Create, start, stop, configure, monitor as desired • Install any software: web, business, client/server, batch processing • Pay only for capacity you use • Variety of cost models Amazon EC2
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EC2 Basics: cost models
On-‐Demand Reserved Spot Dedicated
Pay upfront in exchange for hourly prices that are 50-‐75% lower than
On-‐Demand
Pay for compute capacity by the hour. No long-‐term
commitments
Bid for unused Amazon EC2 capacity
Launch instances in VPC on dedicated customer hardware
Customers can combine mul1ple purchase types to op1mize pricing based on current and forecast capacity needs.
Spiky workloads CommiRed uSlizaSon Time-‐insensiSve workloads Highly sensiSve workloads
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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Provisioning and Lifecycle
• Create -> Start -> Stop -> Terminate • Manually in console • Automate via API (or other tools) • Automatically based on demand
(demand curve)
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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Instance Types GPU
Enabled General Purpose
Storage and IO OpSmized
Compute OpSmized
Memory OpSmized
M3 C3 I2
CG1 M1 C1 CR1 CC2 HI1 HS1
G2
M3 C3 I2 HS1
M2
R3 G2
Added Instance
Types
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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Amazon Machine Images
Your machine images
AMIs you have created from EC2 instances Can be kept private or shared with other
accounts
Amazon maintained
Set of Linux and Windows images Kept up to date by Amazon in each
region
Community maintained
Images published by other AWS users Managed and maintained by Marketplace
partners
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Amazon Machine Images
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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Bootstrapping: metadata and userdata
• Every EC2 Instance has access to local instance metadata and userdata service
Instance request
User data
Instance
Meta-‐data service
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Bootstrapping: metadata and userdata
• Metadata: immutable informa1on about the instance • Accessible from within the instance via HTTP at hQp://169.254.169.254/latest/meta-‐data/
• Script(s) on instance may retrieve useful informa1on about the instance, such as:
• Host name • AMI ID • Instance ID • Public/Private DNS • Availability Zone
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Bootstrapping: metadata and userdata
• User Data: pass up to 16KB of text to an instance on launch • Accessible from within the instance via HTTP at hQp://169.254.169.254/latest/user-‐data/
• Text can be parsed by script on instance and used to configure the machine
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Custom script on AMI
(script_runner.py) fetches userdata,
parses it, and configures EC2 Instance
on boot
Bootstrapping: metadata and userdata
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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 distribu1ons via a package repo or source
Bootstrapping: UserData and CloudInit
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v • UserData to install Apache and MySQL on boot, and aQach 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)
Bootstrapping: UserData and CloudInit
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Bootstrapping
Bake an AMI
Start an instance
Configure the instance
Create an AMI from your instance
Start new ones from the AMI
Configure dynamically
Launch an instance
Use metadata service and cloud-‐init to perform ac1ons on
instance when it launches
Use config management tools like Puppet/Chef/Opsworks
vs
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Bootstrapping
Bake an AMI Configure dynamically
Build your base images and setup custom ini1alisa1on
scripts
Maintain your ‘golden’ base
Use bootstrapping to pass custom informa1on in and perform post launch tasks.
+
Sweet spot
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Bootstrapping: AMIs
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Java App Stack
Example full stack required to run your
application.
Let’s use the 3 bootstrapping
techniques
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Bootstrapping: AMI bake
Fully-functional AMI is pre-build and
ready to launch from the AMI inventory
Inventory of AMIs
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Amazon EC2
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Java AMI
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Bootstrapping: Configure dynamically
Base OS AMI
An AMI with minimal components (OS,
J2EE, and Chef/Puppet) is launched.
All configuration occurs via Chef/
Puppet after instance launch
Inventory of AMIs
Amazon EC2
OS AMI
Fetch on boot
Linux
JEE
Your Code
S3
Hibernate
Tomcat
Log4J
Spring
Struts
Apache
Linux
JEE
Linux
JEE
Chef/Puppet
Chef/Puppet
scripts
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Bootstrapping: Sweet spot
Partially-configured AMI
A “Golden Image” is launched, with
scripts fetching/installing app code
and other supporting components on
boot
Inventory of AMIs
Amazon EC2
Java AMI
Your Code
S3
Log4J
Spring
Struts
Linux
JEE
Hibernate
Tomcat
Apache
Fetch on boot
Fetch on boot
Linux
JEE
Hibernate
Tomcat
Apache
Linux
JEE
Hibernate
Tomcat
Apache
Linux
JEE
Hibernate
Tomcat
Apache
Linux
JEE
Hibernate
Tomcat
Apache
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Why do this?
Automa1on Less fingers, less mistakes
Availability Drive higher
availability with self-‐healing
Security Instances locked down by default
Flexible Shell,
Powershell, CloudForma1on,Chef, Puppet, OpsWorks
Scale Manage large scale
deployments and drive autoscaling
Efficiency Audit and manage your estate with less 1me & effort
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Do Don’t
Some dos and don’ts
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)
L
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
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Monitoring EC2 with CloudWatch
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EC2 Basics Instance Lifecycle EC2 Instance Types Using Amazon Machine Images Bootstrapping EC2 Instances Monitoring EC2 with CloudWatch Autoscaling
![Page 33: Leveraging Elastic Web-Scale Computing with AWS](https://reader030.fdocuments.us/reader030/viewer/2022032422/55a93e2c1a28abae758b4819/html5/thumbnails/33.jpg)
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Types of Scaling
• Ver1cal Scaling • Changing instance size • Increasing EBS Capacity
• Horizontal Scaling • Adding / removing instances • ELB • Autoscaling
r3.8xlarge
c3.2xlarge
m3.medium
m3.medium m3.medium m3.medium
m3.medium m3.medium m3.medium
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Ver-cal Scaling
• Different EC2 instance type • High memory instances • High CPU instances • High I/O instances • High storage instances
• Easy to change instance sizes • Will hit an endpoint eventually • Requires instance to be stopped
r3.8xlarge
c3.2xlarge
m3.medium
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Tradi-onal IT Usage PaJerns
On and Off Fast Growth
Variable peaks Predictable peaks
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Tradi-onal IT Usage PaJerns
On and Off Fast Growth
Variable peaks Predictable peaks
Poor Service
WASTE
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Cloud IT Usage PaJerns (Auto Scaling)
On and Off Fast Growth
Variable peaks Predictable peaks
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Auto Scaling • Automa1c 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 Instruc1ons
Auto Scaling Group
Queue Metrics
Auto Scaling
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Describes what Auto Scaling will create when adding
Instances -‐ Similar to ec2-‐run-‐instances API command
AMI
Instance Type Security Group Instance Key Pair
Only one ac1ve launch configura1on at a 1me
Auto Scaling will terminate instances with old launch
configura1on first rolling update
Auto Scaling managed grouping of EC2 instances
Automa1c health check to maintain pool size
Automa1cally scale the number of instances by policy – Min, Max,
Desired
Automa1c Integra1on with ELB
Automa1c distribu1on & balancing across AZs
Parameters for performing an Auto Scaling ac1on
Scale Up/Down and by how much
ChangeInCapacity (+/-‐ #) ExactCapacity (#)
ChangeInPercent (+/-‐ %)
Cool Down (seconds)
Policy can be triggered by CloudWatch events
Launch Configuration Auto-Scaling Group Auto-Scaling Policy
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Scaling plan
• Scale based on demand • Manual scaling • Scale based on schedule • Maintain current instance levels at all 1me
Auto Scaling
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Auto Scaling Lifecycles
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Autoscaling
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Autoscaling
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Autoscaling
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Autoscaling
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Autoscaling
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Availability Zone A Availability Zone B
Autoscaling: Auto Scaling Group
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Availability Zone A Availability Zone B
Autoscaling: Auto Scaling Group
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Availability Zone A Availability Zone B
Autoscaling: Auto Scaling Group
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Availability Zone A Availability Zone B
Autoscaling: Auto Scaling Group
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Availability Zone A Availability Zone B
Autoscaling: Auto Scaling Group
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v Latency
CloudWatch Auto Scaling
ELB
Auto scaling Group
Autoscaling: ELB + CloudWatch
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v • Tools Used:
• CloudForma1on script – • Create a mul1-‐AZ, load balanced and Auto Scaled sample web site running on an Apache Web Server (m1.small). The applica1on is configured to span all Availability Zones in the region and is Auto-‐Scaled based on the CPU u1liza1on of the web servers.
• Bees with Machine Guns – Performance tes1ng tool • A cloudforma1on script that spins up a dis1buted performance tes1ng tool based on apache eb tool. This tool will hit the ELB with 1000’s of concurrent requests for a total of 100’s of thousands of request, thus loading the web server behind the ELB.
• Expected result • The Apache web server will scale to serve traffic without any customer impact.
Autoscaling: DEMO
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v • CloudForma1on script (Auto scaling apache web server) • Auto-‐scaling group configura1on:
• Min: 1 • Max: 3 • Cooldown: 300
• Scaling Policies: • Scaling Up:
• CPU U1liza1on > 20% for 1 consecu1ve period of 60 seconds • Ac1on: Add 1 instance • Then wait: 60 seconds before next opera1on
• Scaling Down: • CPU U1liza1on < 10% for 2 consecu1ve periods of 60 seconds • Ac1on: Remove 1 instance • Then wait: 60 seconds before next opera1on
• Bees with Machine guns(NASTY)
Demo Information
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Autoscaling isn’t one size fits all
• 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 par1ally scale back shortly aser scaling up, increasing costs.
• Scale too 1midly • Poor performance • Outages due to lack of capacity
• Scale out early / Scale in slowly
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Stop doing these: Provisioning and fixing servers
Trea1ng compute as physical things Thinking of compute as a finite commitment
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and start doing these
Security Build systems secure by
default
Elas1city Stateless autoscaling
applica1ons
Replace not fix Build from scratch, don’t
fix something Unconstrained Say goodbye to
tradi1onal capacity planning
Be cost aware Tag resources, play with
instance types
Automa1on Create instances when you need them, drop
them when not
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What’s more?
• AQach / Detach Instances from Auto Scaling Groups • Place instances into Standby State to Troubleshoot • Hold instances in Pending state for installing sosware / retrieve logs • Create an Auto Scaling Group / Launch Configura1on based on a running instance
• Auto scaling Lifecycle hooks
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QuesSons?
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