This document discusses Auto Scaling on AWS. It provides an overview of how Auto Scaling works by launching EC2 instances in response to changing demand while maintaining availability and managing costs. Auto Scaling uses launch configurations, Auto Scaling groups, and scaling policies to dynamically add or remove EC2 capacity based on CloudWatch metrics and alarms. It allows scaling capacity up and down automatically according to conditions defined through scaling policies to maintain application availability and flexibility.

1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Mv – Marcus Vinicius Ferreira / Claick Oliveira
Solution Architect Team, Public Sector, Education
Maio/2019
AWS: AutoScaling
Compute, AutoScaling, DevOps

2. Mv – Marcus Vinicius Ferreira
mvferr@amazon.com
SolutionsArchitect
BR, Public Sector, Education
Previous: Oracle, Sun, Abril, Dinda, NubankMv

3. Claick Oliveira
claicko@amazon.com
SolutionsArchitect
BR, Public Sector, Education
Claick

4. AWS Agenda
AWS é Escala
AWS Compute: EC2
AWS AutoScaling
AWS Automation: DevOps
AWS Casos de Uso

5. AWS is Architected for Government Security Requirements
Certifications and accreditations for
workloads that matter – Compliant Solutions
AWS CloudTrail and AWS Config –
Call logging and configuration
management for governance and
compliance
• Log, review, alarm
on all user actions
• Browse-and-query
database of current
and previous state
of cloud resources
MTCS
https://aws.amazon.com/compliance/

6. What Is (True) Cloud Computing?
The on-demand delivery of IT resources
over public or private networks with zero
up-front costs, no long-term contracts, and
pay-as-you-go pricing
6

7. Service Breadth & Depth
TECHNICAL
& BUSINESS
SUPPORT
Account
Management
Support
Professional
Services
Training &
Certification
Security &
Pricing
Reports
Partner
Ecosystem
Solutions
Architects
ENTERPRISE
APPS
Virtual
Desktops
Sharing &
Collaboration
Corporate
Email
Backup
Regions Availability Zones Points of Presence
INFRASTRUCTURE
Compute Storage DatabasesCDN Networking
CORE SERVICES
HYBRID
ARCHITECTURE
Data Backups
Integrated
App
Deployments
Direct
Connect
Identity
Federation
Integrated
Resource
Management
Integrated
Networking
Access
Control
Identity
Key mgmt &
Storage
Monitoring
& Logs
SECURITY & COMPLIANCE
Auditing
Configuration,
Compliance
Firewalls
Assessment,
reporting
MARKETPLACE
Business
Apps
Business
Intelligence
Databases
DevOps
Tools
NetworkingSecurity Storage
IoT
Rules Engine
Device
Shadows
Device SDKs
Registry
Device
Gateway
DEV & OPSMOBILE SERVICESAPP SERVICESANALYTICS
Data Warehouse
Hadoop/Spark
Data Collection
Machine Learning
Elastic Search
Queuing &
Notifications
Workflow
Search
Email
Transcoding
One-click Deployment
Identity
Sync
Single Integrated
Console
Push
Notifications
DevOps
Application Lifecycle
Management
Containers
Triggers
Resource Templates
API Gateway
Data Analysis
BI
Mobile Analytics

8. AWS é Escala

9. AWS Global Infrastructure
18 Regions – 54 Availability Zones – 114 Edge Locations
Region & Number of Availability Zones
AWS GovCloud (2) EU
Ireland (3)
US West Frankfurt (2)
Oregon (3) London (2)
Northern California (3)
Asia Pacific
US East Singapore (2)
N. Virginia (5), Ohio (3) Sydney (2), Tokyo (3),
Seoul (2), Mumbai (2)
Canada
Central (2) China
Beijing (2)
South America
São Paulo (3)
Announced Regions
Paris, Ningxia

10. Region
New Region
Coming Soon
Edge Location
Region &
Number of Availability Zones#
The Global Infrastructure

11. REGION
~ 2ms latency
AZa
AZc
AZb
Redundant Tier-1 Internet &
Inter-Region Connectivity
TCTC
DC
DC
DC
DC
DCDC
DC
DC
DC

12. Availability Zone 1a Availability Zone 1b
Internet
10.0.0.5
10.0.0.6
10.0.3.17
10.0.3.5
10.0.1.5
10.0.1.25 10.0.1.8
10.0.1.6
VPC Subnet
VPC Subnet
VPC Subnet
Virtual Private Gateway
Customer Gateway
VPN Connection
Internet Gateway
Customer Data
Center
Virtual Private Cloud

13. Deploy however you like
Your
Datacenter
Amazon Web
Services
Fully Featured
Compute
Resource &
Deployment
Management
Common
Controls for
Security &
Access
Integrated
Networking
Data Integration
& Life Cycle
Management
Flexible hybrid options
Comcast’s IT strategy focuses on combining its own data centers and AWS
as the cornerstone of its next-generation TV service, X1. This has allowed
them to rapidly scale interactive, on-demand content to millions of viewers.

14. AWS Compute: EC2

15. G2
GPU
enabled
M5
General
purpose
Memory
optimized
R4
Dense-storage &
High-I/O optimized
C5
Compute
optimized
C4M4
D2 I2
Compute: EC2 Instance Families
T3
Burstable
performance
X1P3
T2

16. Instance generation
c5.xlarge
Instance family Instance size

17. Amazon EC2 Instances
256
128
64
32
16
8
4
2
1
1 2 4 8 16 32 64 128
Amazon EC2 Compute Units
Memory(GB)

18. Instance sizing
c5.18xlarge 2 x c5.9xlarge
≈
4 x c5.4xlarge
≈
8 x c5.2xlarge
≈

19. Machine Power
Cluster compute instances
Implement HVM process execution
Intel® Xeon® processors
10 Gigabit Ethernet –c3 has Enhanced networking, SR-IOV
cc2.8xlarge
32 vCPUs
2.6 GHz Intel Xeon
E5-2670 Sandy Bridge
60.5 GB RAM
2 x 320 GB
Local SSD
c3.8xlarge
32 vCPUs
2.8 GHz Intel Xeon
E5-2680v2 Ivy Bridge
60GB RAM
2 x 320 GB
Local SSD

20. AWS Auto Scaling

21. Typical Weekly Traffic at Amazon.com
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Provisioned capacity

22. November Traffic to Amazon.com
Provisioned capacity
November
76%
24%
Challenge is to efficiently ‘guess’
the unknown quantity of how much
compute capacity you need

23. The Economics of the Cloud are Compelling
Infrastructure
cost $
Time

24. The Economics of the Cloud are Compelling
Infrastructure
cost $
Time
Predicted demand
Key:

25. The Economics of the Cloud are Compelling
Infrastructure
cost $
Time
Large
capital
expenditure
Predicted demand
Traditional hardware
Key:

26. The Economics of the Cloud are Compelling
Infrastructure
cost $
Time
Large
capital
expenditure
Predicted demand
Traditional hardware
Actual demand
Key:

27. The Economics of the Cloud are Compelling
Infrastructure
cost $
Time
Large
capital
expenditure
Opportunity
cost
Predicted demand
Traditional hardware
Actual demand
Key:

28. The Economics of the Cloud are Compelling
Lost
opportunity
Infrastructure
cost $
Time
Large
capital
expenditure
Opportunity
cost
Predicted demand
Traditional hardware
Actual demand
Key:

29. The Economics of the Cloud are Compelling
Lost
opportunity
Infrastructure
cost $
Time
Large
capital
expenditure
Opportunity
cost
Predicted demand
Traditional hardware
Actual demand
Automated virtualization
Key:

30. instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 4
Availability Zone bAvailability Zone a
Elastic Load
Balancing
Elastic Load Balancing, CloudWatch, and Auto Scaling
CloudWatch

31. instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 4
Availability Zone bAvailability Zone a
Elastic Load
Balancing
Elastic Load Balancing, CloudWatch, and Auto Scaling
CloudWatch

32. instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 4
Availability Zone bAvailability Zone a
Elastic Load
Balancing
Elastic Load Balancing, CloudWatch, and Auto Scaling
CloudWatch

33. instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 6
instanceinstance
Availability Zone bAvailability Zone a
Elastic Load
Balancing
CloudWatch
Elastic Load Balancing, CloudWatch, and Auto Scaling

34. instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 6
instanceinstance
Availability Zone bAvailability Zone a
Elastic Load
Balancing
CloudWatch
Unhealthy Instances Get Replaced…

35. Unhealthy Instances Get Replaced…
instance instanceinstance instance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 6
instanceinstance
Availability Zone bAvailability Zone a
Elastic Load
Balancing
CloudWatch

36. …In a Different AZ if Necessary
instanceinstance instanceinstance
Auto Scaling group
Minimum = 2 Maximum = 10
Desired # of instances = 6
instance
Availability Zone bAvailability Zone a
instance
Elastic Load
Balancing
CloudWatch

37. Capacity matching
Elastic Cloud-Based Resources
Actual demand
Resources scaled to demand
Waste Customer
Dissatisfaction
Actual Demand
Predicted Demand
Rigid On-Premises Resources

38. AWS Storage: EBS e S3
172.31.0.0/16
sa-east-1a sa-east-1b sa-east-1c

39. Multi-AZ Architecture
User Amazon
Route 53
Internet Gateway
Public Subnet
Private Subnet
Public Subnet
Private Subnet
Private Subnet
Private Subnet
Private Subnet
BI / OLAP
Public load
balancer
Private load
balancer
PROD / OLTP

40. AWS AutoScaling: Components

41. Elastic Load Balancing, CloudWatch, and Auto Scaling
Latency
CPU Utilization
CloudWatchAuto Scaling
Elastic Load
Balancing
Auto Scaling group
Execute
Lauch
Configuration

42. How Does Auto Scaling Work?
Launch
Configuration
1
Auto Scaling
Group
Auto Scaling
Policy
Scheduled
Action
2
3
Launch configuration
defines:
• Name
• AMI
• Instance type
• User data
• Security groups
• IAM role
• Etc.
Auto Scaling group defines:
• Name
• Launch configuration name
• Min & Max
• AZ or subnet
• Load balancer
• Desired capacity
• Etc.
Specifies when to dynamically
increase or decrease Amazon
EC2 instances based on
CloudWatch alarms
Tells Auto Scaling to perform a
scaling action at a certain time
in the future (minimum,
maximum, and desired size for
the ASG)
EC2AMI
Auto Scaling group
Load balancer
Auto Scaling group
? ?
1..N
1..20
What
Where
When

43. How Do You Decide on Minimum Capacity Size?
Auto Scaling group
Availability Zone 1 Availability Zone 2
Auto Scaling group defines:
 Desired capacity
 Minimum capacity
 Maximum capacity
Do you have to specify
desired capacity?
What would be a good
minimum capacity to set it
to?
What would be a good
maximum capacity to set it
to?
?
Auto Scaling group
Availability Zone 1
What about HA?
Minimum = 2 instances (# of AZs)
Desired capacity = 2 instances (Min.)
0 or 1?

44. Maximum Capacity Size and Auto Scaling
Scenario:
Auto Scaling Group:
 Minimum = 2
 Maximum = 12
Auto Scaling Policy:
 When CPU utilization is
greater than 60%
 Add 100% of group
= double the capacity
Availability Zone 2Availability Zone 1
Auto Scaling group
CPU utilization triggers the alarm: capacity is doubled until
CPU utilization drops below 60% or max capacity is reached.

45. AWS Building AMIs

46. AMIs and Boot Times
Remember the AMI balancing act!
Test various configurations to find what best meets your
baseline performance.
OS-Only AMI
• More dynamic
• Slower boots
Full AMI
Partially Configured AMIs
• Less dynamic
• Faster boots
Balance between ease of
new deployments and boot
load times

47. AMI Creation Models
Inventory of AMIs
Golden AMI – Fetch
Binaries on Boot
JeOS AMI and Library of
Recipes (Install Scripts)
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Linux
JEE
Your Code
Log4J
Spring
Hibernate
Struts
Tomcat
Apache
Amazon EC2
L
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A
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Amazon EC2
Amazon EC2
Your Code
Amazon S3
Log4J
Spring
Struts
Linux
JEE
Hibernate
Tomcat
Apache
Linux
JEE
Your Code
Amazon
S3
Hibernate
Tomcat
Log4J
Spring
Struts
Apache
L
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Linux
JEE
Linux
JEE
Chef/Puppet
Chef/Puppet
Scripts
Java AMI
Java App Stack
Java AMI JeOS AMI
Fetch on boot
Fetch on boot
Fetch on boot
Minimal provisioning Partial provisioning on boot Full provisioning on boot

48. Packer.io
https://www.packer.io/downloads.html

49. Ready. AMI. Fire!
Linux AMI EC2: build machine.
• Size: Medium
• Run: repo update -y
• Add: pkg: apache
• Add: pkg: php
• Add: pkg: mod_php
• Add: pkg: memcache-client
• Add: git checkout: my-app-release-1.2
• Add: wget: app/config.php
• Add: wget: conf.d/my-app.conf
Customer AMI
• Name: my-app-1.2
Your LAN
Segments
Dev
QA
Prod
Packer and command-line tools.

50. AMI Approach Use Case: Netflix
Uses a "tiered AMI" system
with layered prerequisites.
Foundation AMI
(monitor agent, etc)
Base AMI
(Java)
Application AMI
(release 1.1)
AMI provided by AWS
Basic tools and
system updates
Core software and
performance
optimizations
App-specific AMI
generated by Jenkins
CI platform
AWS Linux AMI
(Public AMI)
Base AMI
(Ruby
Base AMI
(Python)
Application AMI
(release 1.2)
Application AMI
(release x.x)
Application AMI
(release y.y)

51. Packaging/baking AMIs
#1 reason to bake is to decrease your boot
time
 Software packages that require painful/long setup
 Standard software that must be there at startup
 Any configuration items that cannot be remotely sourced or automated
Strike a balance between those things that
change often and those that don’t
AWS provides easy interfaces to create the
AMI or import the AMI
Third-party tooling can be helpful
• Packer (includes Linux and Windows)
https://packer.io/
AMI Instances
Tip: Starting from an existing
Amazon-provided image is
recommended. Once done
customizing, you should stop
the instance and capture the
AMI.

52. AWS Automation: DevOps

53. Infrastructure Management

54. Cloudformation

55. DevOps: What is AWS CloudFormation?
Declarative programming language for deploying AWS resources.
Uses templates and stacks to provision resources.
Create, update, and delete a set of resources as a single unit (stack).
Create/delete
AWS CloudFormation
Create/delete AWS
resources
Template Stack
- Basic definition of
resources to create
- JSON text file
- Collection of AWS
resources

56. Example
Environment
Templates
Dev Apps
Stack
Dev Base
Stack
Test Apps
Stack
Test Base
Stack
Private
Subnet
App tier
Private
Subnet
DB tier
Master
Public
Subnet
Private
Subnet
Web tier
Private
Subnet
App tier
Private
Subnet
DB tier
NAT
Master
AMIs Amazon EBS
snapshots
Internet Gateway Internet Gateway
Development Account Production Account
Private
Subnet
Web tier
NAT
Public
Subnet

57. Cloudformation to the RESCUE!
AWS VPC
Your LAN
Segments
AMI for Python
AMI for Perl
AMI for Java
Remember: DO NOT share your machines!

58. Integration with the cloud
Your Data Center
Project A
Dev

59. Dedicated Infrastructures
Your Data Center
AWS VPC
Physical Cluster
C++/Fortran
Bio Informatics
Perl
Engineer
Python
Physics
Java
… and use dedicated clusters
for specific software solutions

60. Many Environments
Development
QA 1
QA 2

61. Red-Black Deployment: Instant Cutover
Web Server Fleet
(Amazon EC2)
…..
Load Balancing
(Elastic Load Balancing)
v1.1
v1.1
v1.1
v1.1
v1.1
v1.1
Persistent Layer
(Databases and S3)

62. Red-Black Deployment: Pre-Cutover
Web Server Fleet
(Amazon EC2)
…..
Persistent Layer
(Databases and S3)
Load Balancing
(Elastic Load Balancing)
v1.2
v1.2
v1.2
v1.2
v1.2
v1.2
v1.1
v1.1
v1.1
v1.1
v1.1
v1.1

63. Red-Black Deployment: Cutover to New System
Web Server Fleet
(Amazon EC2)
…..
Load Balancing
(Elastic Load Balancing)
v1.2
v1.2
v1.2
v1.2
v1.2
v1.2
v1.1
v1.1
v1.1
v1.1
v1.1
v1.1
Persistent Layer
(Databases and S3)

64. Red-Black Deployment: Cutover to New System
Web Server Fleet
(Amazon EC2)
Load Balancing
(Elastic Load Balancing)
v1.2
v1.2
v1.2
v1.2
v1.2
v1.2
Persistent Layer
(Databases and S3)

65. Embracing Failure: Fault Injection
Build a strong test harness to force out-of-spec
failures to surface.
• Refuses all connections.
• Reads requests at 1 byte/second.
• Accepts request, and sends responses at
1 byte/second rate.
• …etc.
Inject failures regularly into your systems under
controlled circumstances, using third-party tools
such as Netflix Simian Army which includes
Chaos Monkey, Chaos Gorilla, etc.

66. Components:
• EC2 Linux Auto-Scaling
• RDS MySQL Multi-AZ
• Elastic Load Balancer
• S3 Bucket
AWS: WordPress Reference Architecture

67. AWS Elastic Beanstalk é a ferramenta orquestrador que executa
um deploy a partir do Git, numa infra-estrutura em Auto-Scaling.
AWS: Git, Elastic Beanstalk, Architecture

68. Diferentes Sites terão:
• Um repositório Git específico
• Uma infra-estrutura Auto-Scaling dedicada
• Uma rotina de deploy independente
Múltiplos Sites: Git, Elastic Beanstalk, Deploy
WebSite 1
WebSite 2
WebSite 3
WebSite 1
WebSite 2
WebSite 3

69. AWS Beanstalk and Wordpress
https://aws.amazon.com/getting-started/projects/build-wordpress-website/

70. Questions?
Mv – mvferr@amazon.com

71. Website!

72. Obrigado!
Mv – mvferr@amazon.com