Running your Amazon EC2 instances in Auto Scaling groups allows you to improve your application's availability right out of the box. Auto Scaling replaces impaired or unhealthy instances automatically to maintain your desired number of instances (even if that number is one). You can also use Auto Scaling to automate the provisioning of new instances and software configurations as well as to track of usage and costs by app, project, or cost center. Of course, you can also use Auto Scaling to adjust capacity as needed - on demand, on a schedule, or dynamically based on demand. In this session, we show you a few of the tools you can use to enable Auto Scaling for the applications you run on Amazon EC2. We also share tips and tricks we've picked up from customers such as Netflix, Adobe, Nokia, and Amazon.com about managing capacity, balancing performance against cost, and optimizing availability.

1. AWS Console for iOS and Android
View resources on the go!
Supports EC2, ELB, RDS, and Auto Scaling.
We are rapidly building support for more services.
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Amazon Appstore: http://bit.ly/consoleAmazon
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2. More Nines for Your Dimes:
Improving Availability and Lowering Costs Using
Auto Scaling and Amazon EC2
Derek Pai, AWS
Cameron Stokes, The Weather Channel
Keith Baker, Nokia
Laurent Rouquette, Adobe
Brandon Adams, Dreambox Learning
November 15, 2013
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

3. Some topics we’ll cover today
• The Weather Channel
– Maintaining application response times and fleet utilization rates
– Handling cyclical demand and unexpected “weather events”
• Nokia
– Static Auto Scaling for non-critical applications
– Auto Scaling for 99.9% Uptime
• Adobe
– Cost control and asymmetric scaling responses
– AWS CloudFormation, custom scripts, and multiple inputs
• Dreambox
– Using performance testing to choose scaling strategies
– Dealing with bouncy or steep curves

4. Cameron Stokes, The Weather Channel
Keith Baker, Nokia
Laurent Rouquette, Adobe
Brandon Adams, Dreambox Learning
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

5. Who We Are

6. Who We Are

7. Who We Are
•
•
•
•
Top 30 web property in the U.S.
2nd most viewed television channel in the U.S.
85% of U.S. airlines depend on our forecasts
Major retailers base marketing spend and store
displays based on our forecasts

8. Who We Are
“The Weather Channel has an unduplicated
audience of 163 million unique visitors monthly
across The Weather Channel TV and weather.com
platforms.”
- Frank N. Magid Associates, Q3 2012

9. Why AWS?

10. Why AWS?
It’s hurricane season in 2012.
We’re capacity constrained.
What can we do…

11. Why AWS?
Identify “mobile” workload
and move it to AWS...
Radar images on
wunderground.com.

12. Why AWS?
Self-contained
application
Easy to replicate the
datastore

13. Why Auto Scaling?

14. Why Auto Scaling?

15. Why Auto Scaling?

16. Why Auto Scaling?

17. Why Auto Scaling?
Hurricane Sandy

18. Radar on AWS

19. Radar on AWS

20. Radar on AWS
CPU Utilization

21. Radar on AWS
Host Count

22. Radar on AWS

23. Radar on AWS

24. Radar on AWS

25. Challenges

26. Challenges
Your monitoring tools
probably suck

27. Challenges
Rolling out application
updates

28. Challenges
Zone failures

29. Challenges
Zone failures

30. Challenges
Zone failures

31. Challenges
Lots of knobs and
switches to get right
…or wrong

32. Cameron Stokes, The Weather Channel
Keith Baker, Nokia
Laurent Rouquette, Adobe
Brandon Adams, Dreambox Learning
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

33. Maintaining non-critical
applications with Static
Autoscaling

34. Clickwork Annotation
•
•
•
•
•
•
Internal annotation application
~100 users
All hourly contractors
Downtime is acceptable
Port of existing single-server application
Perfect first step into AWS

35. Architecture
www.clickworksite.com
S3 Repository
Bucket
S3 Backup
Bucket
Django Web App
Postgresql DB
EBS Volume 1
Manual Taggers
Elastic Load Balancing
(SSL Termination)
EC2 Instance
EBS Volume 2
Data Volume
Auto Scaling Group
Cloudwatch
Availability Zone #1
Periodic Snapshots

36. Clickwork Details
• Single AZ, Single Instance maintained by
Autoscaling. Instance failures take ~15 min to
recover.
• Upgrades are done with planned downtime via a
fully scripted re-deployment also ~15 min unless
database schema is updated.

37. Clickwork Details
• While uptime is flexible our data is precious
– 2 EBS Volumes in Software RAID 1
– Periodic Postgres backups to S3
• Daily for first 18 months
• Increased to hourly
– Manual EBS snapshots today

38. Clickwork Details
• Using Auto Scaling means the system cannot be
configured by hand in any way and has to recover itself
• Deployment uses Masterless Puppet
– Puppet tree built into an RPM & pushed into S3 as Yum Repo
– CloudInit
• Set up S3 Yum Repositories
• Install Puppet RPM
• Run Puppet
– Puppet
•
•
•
•
Installs needed packages
Mounts EBS volumes (id’s stored in launch config data)
Configures / Mounts RAID 1
Starts Postgres & Apache

39. Clickwork Results
• Successful deployment of Nokia’s first AWS application
• Drastically Increased uptime over ‘server in a closet’
– Previously, outages exceeded 2 days each
• A/C
• Power
• Network
– Design goal was max 1 day outage
– 2 outages over 1 hour
• 2 hours (8.25.2013 EBS Event)
• 9 hours (10.22.2012 EBS Event)

40. Autoscaling for 99.9% Uptime

41. Local Search
• First customer facing application on AWS
• Obvious need for Uptime

42. Local Search Architecture
US-West-2
Frontend
Group
Zookeeper1
Zookeeper1
Frontend Group
Zookeeper2
EU-West-1
Zookeeper2
AP-Southeast-1
Backend
Groups
Zookeeper3
Zookeeper3
US-East-1a
US-East-1
Backend Groups
US-East-1b

43. Local Search Single Instance Auto
Scaling Groups
• Instances in 1 node Auto Scaling groups automatically
register themselves in DNS via Route53 based on their
Auto Scaling group’s name.
• Auto Scaling group names are formed as a pattern with
cluster name and node type so a cluster1-frontend node
knows to look for cluster1-zookeeper1, cluster1zookeeper2…
• An alternative was querying the Auto Scaling group for
instances. Using DNS allowed us to use more standard
tools.

44. Local Search Zookeepers
•
•
•
•
•
•
•
•
Three single instance Auto Scaling groups
Zookeeper needs a set, consistent list of servers across all servers (zookeeper1,
zookeeper2, zookeeper3)
At launch, zookeeper didn’t re-resolve hostnames on the server side. We proxy Zookeeper
through localhost inetd & netcat to cause a DNS lookup on each connection
Client had been fixed at this point
Requires a short DNS timeout but rather low usage (only on reconnects)
Considered one zookeeper per AZ. One region we deploy in only has two AZs so we can’t
lose either AZ and maintain quorum. So we run one cluster in each AZ.
We have lost all three zookeepers in one AZ, which caused one AZ to go offline. This
failure was handled by the ELB. Auto Scaling fixed this in ~15 min.
Avoided EIPs

45. Local Search Logging
•
•
•
•
•
•
Again a single instance Auto Scaling group
Buffers logs for entire cluster
Does compression, encryption, and uploads to S3
Forwards to central logging system for analysis
Single point of investigation for entire cluster
All instances also run scribe to buffer if logging node
is lost

46. Local Search Updates
• Moving to a job that monitors the available data vs. the present data
• When new data is present the job takes a lock in zookeeper for
cluster size changes. It then increases the node count and waits for
the Auto Scaling message that capacity has changed. It unlocks the
cluster size changes and monitors the loading of the new node.
• Other nodes can then lock and modify the capacity to get their own
replacement nodes
• Once new nodes are loaded, the old nodes take a lock and decease
the cluster size with a targeted kill to themselves
• Care taken to track a failed replacement node

47. Local Search Success
• All detected health problems have been
successfully replaced by autoscaling with zero
intervention. We’ve been paranoid so it still
pages us, it’s beginning to feel silly
• Zookeeper setup has performed flawlessly
• Undetected problems are our biggest challenge
– Disk failure
– Intermittent connectivity failures

48. Cameron Stokes, The Weather Channel
Keith Baker, Nokia
Laurent Rouquette, Adobe
Brandon Adams, Dreambox Learning
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

49. Intro – Cloud Ops

50. Goals
• Meet demand
• Control costs
• Maintain capacity

51. Architecture

52. Scaling the web layer
• Number of HTTP requests
• Average CPU load
• Network in/out

53. Scaling the worker layer
• SQS queue length, specifically "approximate
number of messages visible"

54. Scaling Down: who wants to volunteer?
• Know your usage
• Scale up fast, scale down slow

55. Cost Control
• Scheduled scaling: we analyzed our traffic and
picked numbers.
– scale up in the morning, scale down in the evening
• Policies for slow scale down
• Stage environments: downscale everything to
“min-size” daily (or more)

56. How – AWS CloudFormation
"ScaleUpPolicy" : {
"Type" : "AWS::AutoScaling::ScalingPolicy",
"Properties" : {
"AdjustmentType" : "ChangeInCapacity",
"AutoScalingGroupName" : { "Ref" : "WorkerAutoScalingGroup" },
"Cooldown" : {"Ref": "cooldown"},
"ScalingAdjustment" : { "Ref" : "adjustup" }
}
},
"WorkerAlarmScaleUp": {
"Type": "AWS::CloudWatch::Alarm",
"Properties": {
"EvaluationPeriods":{"Ref" : "evalperiod"},
"Statistic": "Sum",
"Threshold": {"Ref" : "upthreshold"},
"AlarmDescription": "Scale up if the work load of transcode queue is high",
"Period": {"Ref" : "period"},
"AlarmActions": [ { "Ref": "ScaleUpPolicy" }, { "Ref" : "scalingSNStopic" } ],
"Namespace": "AWS/SQS",
"Dimensions": [ { "Name": "QueueName", "Value": {"Ref" : "queuename" }}],
"ComparisonOperator": "GreaterThanThreshold",
"MetricName": "ApproximateNumberOfMessagesVisible"
}
},

57. How – custom scripts
. . .
[2013-10-08T00:00:14.31] INFO -- : Scaling Worker1 with CAPACITY 10 (schedule=BIZPM)
[2013-10-08T00:00:24.70] INFO -- :
PROD-A-Worker1-3276-ASG-1VUVI7JI1M19U has max size 0; most likely not active
environment; no action taken
PROD-B-Worker1-3484-ASG-H2QX6HC82SLQ is active; resetting capacity: current: 20; new:
10
Result: OK-Desired Capacity Set
[2013-10-08T00:00:24.70] INFO -- : Scaling Worker2 with CAPACITY 7 (schedule=BIZPM)
[2013-10-08T00:00:34.98] INFO -- :
PROD-A-Worker2-3275-ASG-LY3NHN8C7A82 has max size 0; most likely not active
environment; no action taken
PROD-B-Worker2-3483-ASG-70MUBNNP3QV0 is active; resetting capacity: current: 15; new: 7
Result: OK-Desired Capacity Set
. . .

58. How – Custom Metrics
. . .
Sat Oct 6 05:51:03 UTC 2012
Number of AZs: 4
Number of Web Servers: 16
Number of Healthy Web Servers: 16
ELB Request Count: 9523.0
Request Count Per Healthy Web Server: 595.1875
Network In Per Healthy Web Server: 51 MB
Network Out Per Healthy Web Server: 1 MB
CPU Per Healthy Web Server: 25.23875
Publishing Custom Metrics: InstanceRequestCount, HealthyWebServers,
InstanceNetworkIn, InstanceNetworkOut, InstanceCPUUtilization to namespace
WebServer in us-east-1
. . .

59. How – multi-input scaling
• Scale up
• +2 instances if more than 50 visible messages for more than 5 min
• +50% instances if more than 1000 msg for more than 2 min
• + fixed 100 instances if more than 10,000 msg for more than 1 min
• Scale down
• –10 instance if 0 msg for more than 10 min
• – 25% if 0 msg for more than 30 min

60. Advice
•
•
•
•
•
Use CloudFormation!
Know your system
Watch your scaling history
Scaling up is easy, scaling down not so much
Mantra: scale up fast; scale down slow

61. Cameron Stokes, The Weather Channel
Keith Baker, Nokia
Laurent Rouquette, Adobe
Brandon Adams, Dreambox Learning
© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

62. Scaling strategies we use
1. Scaling with CloudWatch alarms
2. Scheduled scaling
3. Scaling with multiple CloudWatch alarm
conditions

63. A little background on our application
• Ruby on Rails
• Unicorn
• We teach kids math!

64. A workload well suited for auto scaling

65. Scaling with CloudWatch alarms

66. What’s an alarm?
• Measures some
metric in CloudWatch
• Go above or beyond
a threshold, alarm
fires
• Which can trigger an
autoscaling action

67. Performance test to get a baseline
• Discover the ideal number of
worker processes per server
– Too few and resources go
unused
– Too many and performance
suffers under load
• Obtain the maximum load
sustainable per server
– Our performance tests
measures number of concurrent
users
• Find the chokepoint
– For us, this was CPU utilization

68. Performance testing

69. Identify the breaking point
Breaking point was at about 400 users per server

70. Our first method to find scale points
• Provision a static amount
of servers that we know
can handle peak load
• Adjust scale up and scale
down alarms based on
observed highs and lows
• This worked, but was
super inefficient, both in
time and money spent

71. Let’s do some math – identify variables
Independent
• Concurrent users
Dependent
• CPU utilization
• Memory utilization
• Disk I/O
• Network I/O

72. Let’s do some math – find the slope
• Adding about 1600 users per hour
• Which is about 27 per minute
• We know that we can handle a max of
about 400 users per server at 80% CPU
usage
• Which is about 0.2% CPU usage per user

73. Let’s do some math – when to scale?
• We know (from other testing) that it takes
us about 5 minutes for a new node to
come online
• We’re adding 27 users per minute
• Which means we need to start spinning
up new nodes when we’re about 135
users (27 x 5) per node short of max
• Which is at about 53% utilization:
(80% – (0.2% * 135))

74. Scaling point equations
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑢𝑠𝑒𝑟𝑠 𝑝𝑒𝑟 𝑛𝑜𝑑𝑒 − (𝑢𝑠𝑒𝑟𝑠 𝑎𝑑𝑑𝑒𝑑 𝑝𝑒𝑟 𝑚𝑖𝑛𝑢𝑡𝑒 ∗ 𝑠𝑝𝑖𝑛 𝑢𝑝 𝑡𝑖𝑚𝑒)
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 400 − (27 ∗ 5)
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 265 users per node
𝑢𝑠𝑒𝑟𝑠
𝑢𝑠𝑒𝑟𝑠
=
−
𝑛𝑜𝑑𝑒
𝑛𝑜𝑑𝑒
𝑢𝑠𝑒𝑟𝑠
𝑚𝑖𝑛𝑢𝑡𝑒𝑠
∗
𝑚𝑖𝑛𝑢𝑡𝑒
𝑛𝑜𝑑𝑒
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 𝑢𝑠𝑒𝑟𝑠 𝑝𝑒𝑟 𝑛𝑜𝑑𝑒 ∗ 𝑐𝑝𝑢 𝑝𝑒𝑟 𝑢𝑠𝑒𝑟
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 265 ∗ .2
𝑠𝑐𝑎𝑙𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡 = 53% cpu per node
𝑐𝑝𝑢
𝑢𝑠𝑒𝑟𝑠 𝑐𝑝𝑢
=
∗
𝑛𝑜𝑑𝑒
𝑛𝑜𝑑𝑒 𝑢𝑠𝑒𝑟

75. How much to scale up by?
• The lowest we can scale up by is 1 node per AZ;
otherwise, we would be unbalanced
• For us, this is an extra 800 users of capacity in
five minutes, plenty enough to keep up with our
rate of adding 1600 users per hour
• Adding 800 users of capacity every five minutes,
we could support 9600 additional users per hour

76. Evaluate your predictions
• In the real world, we’ve inched up from
scaling at 53%
• Our perf test is a little harsher than the
real world
• Numbers derived from the perf test are
only as accurate as the simulation of
traffic you specify in your perf test

77. Scheduled scaling

78. Acceleration in load is not constant
Request count for a 24 hour period

79. We can’t use one size fits all
• Scale too aggressively
– Overprovisioning:
increases cost
– Bounciness: we add more
than we need and have to
partially scale back shortly
after scaling up, which
increases cost
• Scale too timidly
– Poor performance
– Outages due to lack of
capacity

80. Bounciness and steepness
• Add scheduled scaling
points to eliminate
bounciness
• Scheduled scale for the
steepest points of your
demand curve
• Let dynamic scaling
take care of the less
steep parts of the curve

81. Scaling curve before…
min
min
min
min
min

82. …and after
min
min
min
min
min

83. Scaling with multiple
CloudWatch alarm conditions

84. The need for multiple alarms
• Sometimes we get an unexpected spike in load
that can’t be handled by our normal scaling rules
• We thought we could just add another level of
alarm at a higher CPU utilization level
• However…

85. Difficulty with multiple alarms
• There is no mechanism to
choose which alarm triggers
an autoscaling action when
multiple alarms are
triggered
• Once an autoscaling action
is triggered by an alarm, we
enter the cool down period,
precluding other alarms
from triggering autoscaling
actions

86. The solution
• Multiple alarms, multiple
groups
• We have a “high demand”
group that normally has
zero instances
• If we reach a certain
threshold, the high
demand group scales up
to cover the load that the
regular group can’t handle

87. Putting it all together

88. Demand curve hugs the usage curve…

89. …and a (mostly) flat response curve

90. Please give us your feedback on this
presentation
CPN201
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