This session dives deep into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending, often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily-applicable methods for cost optimizing Amazon EC2, Amazon S3, and a host of other services to save you time and money.

1. CPN211 - Reducing Cost and Maximizing
Efficiency: Tightening the Belt on AWS
Tom Johnston - Business Development Manager, Amazon Web Services
Sean Simpson - Director of Operations, Stitcher, Inc.
Kingsley Wood - Business Development Manager, Amazon Web Services
Ashay Padwal - CTO, Vserv.mobi
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.

2. Introductions and Outline
• Tom Johnston (AWS)
Reducing Cost and Spending Smart
• Sean Simpson (Stitcher)
Moving to AWS – A Story
• Kingsley Wood (AWS)
Maximizing Efficiency and Cost Optimization
• Ashay Padwal (vServ.mobi)
a Spot Case Study

3. Reducing Cost
and
Spending Smart
Tom Johnston – Business Development Manager, AWS

4. Fundamentals
•
•
•
•
•
Explicit Objectives
Match Instances with Workloads
Match Scale & Use with Demand
Match Purchasing with Utilization
Governance Matters

5. Objectives

6. Objectives
AWS provides you the ability to
match your architecture to your
objectives

7. Instance types
Start
Choose an instance
that best meets your
basic requirements
Match memory & virtual
cores

8. Instance types
Start
Tune
Choose an instance
that best meets your
basic requirements
Change instance size up
or down based upon
monitoring
Match memory & virtual
cores
Use CloudWatch &
Trusted Advisor to assess

9. Know your usage
Instance
Free Memory
Free CPU
Free HDD
…
Custom Metrics
…
At 1-min
intervals
PUT
2 weeks
Amazon
CloudWatch
Alarm

10. More
Memory
Memory (GB)
High-Mem
Cluster
Compute
High
Storage
High
I/O
High
Mem
Cluster
Compute
M3
C3
M1
High-CPU
Processing Ability
More
Processing

11. Instance types
Start
Tune
Roll-Out
Choose an instance
that best meets your
basic requirements
Change instance size up
or down based upon
monitoring
Run multiple instances
in multiple Availability
Zones
Match memory & virtual
cores
Use CloudWatch &
Trusted Advisor to assess

12. Choose your metric
optimize for the metric

13. Choose your metric
optimize for the metric
Cost per unit of work per instance(size)
Workload A
Workload B
Workload C
Optimal on 4x
m1.xlarge
Optimal on 10x
m1.medium
Optimal on 2x
m3.xxlarge

14. Choose your metric
optimize for the metric
Cost per unit of work per instance (size)
100 concurrent jobs on 10 x m1.large @ $0.26 / hr = $ 0.026 / job
vs
300 concurrent jobs on 10 x m3.xlarge @ $0.58 / hr = $ 0.019 / job

15. Choose your metric
optimize for the metric
Think workload density
Don’t just focus on instance hourly rate

16. Server Load
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

17. Server Load
Capacity of 1 Server
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

18. Server Load
Traditional capacity required
Capacity of 1 Server
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

19. Server Load
Traditional capacity required
Capacity of 1 Server
1 Server for 8 hours
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

20. Server Load
Traditional capacity required
Capacity of 1 Server
1 Server for 8 hours
1 Server for 8 hours
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

21. Traditional capacity required
Server Load
1 Server for 8 hours
Capacity of 1 Server
1 Server for 8 hours
1 Server for 8 hours
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

22. Traditional capacity required
Server Load
1 Server for 8 hours
Capacity of 1 Server
1 Server for 8 hours
1 Server for 8 hours
1 Server for 8 hours
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

23. Server Load
Traditional capacity required
Capacity of 1 Server
1/3rd
Saving
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of day

24. 6
Instance Count
5
4
3
2
1
0
0
2
4
6
8
10
12 14 16 18
Day of Month
20
22
24
26
28
30

25. 6
Instance Count
5
Monthly
predictable
peak
processing
4
3
2
1
0
0
2
4
6
8
10
12 14 16 18
Day of Month
20
22
24
26
28
30

26. Traditional capacity required
6
Instance Count
5
4
3
2
1
0
0
2
4
6
8
10
12 14 16 18
Day of Month
20
22
24
26
28
30

27. Traditional capacity required
6
Instance Count
5
4
3
2
1
Elastic Capacity
0
0
2
4
6
8
10
12 14 16 18
Day of Month
20
22
24
26
28
30

28. Traditional capacity required
6
Instance Count
5
4
75% Savings
3
2
1
Elastic Capacity
0
0
2
4
6
8
10
12 14 16 18
Day of Month
20
22
24
26
28
30

29. Reserved instances
On-demand instances
Unix/Linux instances start at
$0.02/hour
Pay as you go for compute power
Low cost and flexibility
Pay only for what you use, no up-front
commitments or long-term contracts
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing

30. Reserved instances
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases:
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery

31. Reserved instances
Heavy utilization RI
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases:
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
Up to 58%
Savings

32. Reserved instances
Heavy utilization RI
> 80% utilization
Lower costs up to 58%
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases:
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline

33. Reserved instances
Heavy utilization RI
> 80% utilization
Lower costs up to 58%
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases:
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline
Medium utilization RI
Up to 49%
Savings

34. Reserved instances
Heavy utilization RI
> 80% utilization
Lower costs up to 58%
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline
Medium utilization RI
41-79% utilization
Lower costs up to 49%
Use Cases:
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
Use Cases: Web applications, many heavy
processing tasks, running much of the time

35. Reserved instances
Heavy utilization RI
> 80% utilization
Lower costs up to 58%
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline
Medium utilization RI
41-79% utilization
Lower costs up to 49%
Use Cases: Web applications, many heavy
processing tasks, running much of the time
Use Cases:
Light utilization RI
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
Up to 34%
Savings

36. Reserved instances
Heavy utilization RI
> 80% utilization
Lower costs up to 58%
On-demand instances
Reserved instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Low cost and flexibility
Low Cost / Predictability
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline
Medium utilization RI
41-79% utilization
Lower costs up to 49%
Use Cases: Web applications, many heavy
processing tasks, running much of the time
Use Cases:
Light utilization RI
Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery
15-40% utilization
Lower costs up to 34%
Use Cases: Disaster Recovery, Weekly /
Monthly reporting, Elastic Map Reduce

37. Best RI for Utilization
$18,000
$16,000
$14,000
$12,000
$10,000
$8,000
Heavy
Medium
Light
$6,000
$4,000
$2,000
$-
O-Demand

38. Best RI for Utilisation
$18,000
$16,000
$14,000
$12,000
$10,000
$8,000
Heavy
Medium
Light
$6,000
$4,000
$2,000
$-
O-Demand

39. Optimizing costs with RIs
14
12
On Demand
10
Light Utilization RI
8
Medium Utilization RI
6
Heavy utilization RI
4
2
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

40. Spot instances
On-demand instances
Reserved instances
Spot instances
Unix/Linux instances start at
$0.02/hour
1- or 3-year terms
Bid on unused EC2 capacity
Pay as you go for compute power
Pay low up-front fee, receive significant hourly
discount
Spot Price based on supply/demand,
determined automatically
Low cost and flexibility
Low Cost / Predictability
Cost / Large Scale, dynamic workload handling
Pay only for what you use, no up-front
commitments or long-term contracts
Helps ensure compute capacity is available
when needed
Use Cases:
Applications with short term, spiky, or
unpredictable workloads;
Application development or testing
Use Cases:
Use Cases:
Applications with flexible start and end times
Applications with steady state or predictable
usage
Applications only feasible at very low compute
prices
Applications that require reserved capacity,
including disaster recovery

41. Governance Matters
• Who can create and launch instances?
• Who checks that only needed instances are
running?
• Have specific policies
• Use AWS tools such as IAM to help enforce
them

42. Checklist
•
•
•
•
•
•
Identify your goals
Understand your workload & match to instances
Scale up and down with demand
Align purchasing methods & utilization
Have governance appropriate to your goals
Change in goals & workload will drive change in
use of AWS

44. Moving to AWS – A Story
Sean Simpson
Director of Operations - Stitcher, Inc.

45. What is Stitcher?
• Stitcher is to news and talk radio what Pandora
is to music
• Stitcher is a content aggregator
• Stitcher is an on-demand service
• Stitcher is deployed on mobile, CE, and
automotive platforms

46. Stitcher by the Numbers
•
•
•
•
12 million downloads
20,000+ shows
Over 1 million hours of listening weekly
Over 100 TB outbound data monthly

47. With Growth Comes Pain
• DRBD database locked us into hardware
• Sublease of colocation facility restricted our
access to our servers
• Server leases and purchases constrained our
architecture
• Growth inhibited by human, server, and vendor
resources

48. What options did we consider?
• Move to another colocation facility
• Move to a cloud provider
• Move to a hybrid colocation/cloud provider

49. Why we chose Amazon Web Services
• Familiarity
– Already using Amazon Simple Storage Service for our RSS
feeds
– Already experimenting with Amazon Elastic Compute Cloud
– Recently implemented Amazon Simple Queue Service

50. Why we chose Amazon Web Services
• Flexibility / Scalability
– Ability to adjust resources quickly in our production environment
– Ability to create any number of environments
– Ability to design servers as we wanted with respect to operating
systems, systems software, etc.

51. Why we chose Amazon Web Services
• Cost
–
–
–
–
Cost matches usage
Bandwidth savings when using Amazon CloudFront as our CDN
Many resources to assist in optimization
Put simply, we got our solution for the lowest quote

52. Why we chose Amazon Web Services
• Documentation & Customer Service
– Knowledgeable solutions architects
– “Right-level” documentation
– Quick response to our needs

53. Architecting Change
• Ask yourself: What are we trying to achieve?
• Know yourself, know your systems
• Consider industry best practices (but don’t
blindly follow them)
• Read the documentation

54. Use Puppet or Chef
• Configuration management tools are both
enabling and liberating
• Build, destroy, and build again
• Write once, build many
• Nuances between node types are managed with
clearly written rules
• Naming conventions are your friend

55. Our Architecture

56. Looks nice, but what does it do?
•
•
•
•
•
High Availability
Scalability
Security
Performance
Cost effectiveness

57. The Results – Database connections/sec
Before
225
After
450
0
100
200
300
400
500

58. The Results – GetStationPlaylist()
Before
0.75
After
0.1
0
0.2
0.4
0.6
0.8

59. The Results – Maximum throughput
Before
5000
After
20000
0
5000
10000
15000
20000
25000

60. The Results – Downtime
Before
1200
15
After
0
200
400
600
800
1000
1200
1400

61. Cost Optimization Results
• Twice the results for the same money

62. How we save money
•
•
•
•
•
•
•
Reserved instances
Appropriate instance types
CloudFront CDN
Rapid reorganization using the API
Monitor utilization
Load test
Housecleaning

63. On Deck Cost Savings
•
•
•
•
Spot instances for processing tasks
Auto Scaling
In-app optimizations
Instance type tuning

64. Parting Advice
• Architect for 10X
• Take the time to get it right the first time (or at
least, close enough)
• Plan on continuous evolution of systems

65. Maximizing Efficiency
and
Cost Optimization
Kingsley Wood – Business Development Manager, AWS

66. Considerations
•
•
•
•
•
•
Offloading – reduce footprint
Utilization – your biggest lever
Managed Services – leverage RDS, SQS, SES
Consolidated Billing – pooling resources
Flexible Evolution – continually revisit
Spot Instances – think big, new possibilities

67. OFFLOAD all static content
• reduce your compute demand and costs
• improve end-user experience
• increase reliability and durability
+

69. ENTIRE SITE via CloudFront
• minimize client-server chatter (keep it at the edge)
• reduce server-database traffic (cache the common calls)
• speed up mobile app response (persistent connections)
+

70. Real World Example
Standard Setup
Optimized
• 4 x Medium Instances
$485
• AWS Data Transfer 1 TB
$194
• 1 x Medium Instance
$121
• CloudFront Data 1 TB
$168
• CloudFront Requests
$1.89
• Total = $291
• Total = $679
57% Lower Cost + 6X Faster

75. Offloading Tips
• Leverage S3, CloudFront, Route 53
• Eliminate repeated calls (edge and data cache)
• Static website hosting on S3
No web server at all!
• Minimize your EC2 and database footprint
stand up Read Replicas for variable loads

76. Utilization and Auto-Scaling: Granularity
more small instances vs. less large instances
29 Large @
$0.32/hr
= $9.28
59 Small @
$0.08/hr
= $4.72

77. Utilization – Trigger Actions by Event
Leverage CloudWatch to collect and measure metrics

78. Buuuk for Singapore Press Holdings (SPH)

79. The Straits Times Mobile App
REAL-TIME reaction response
•
•
•
•
notification of pending News Flash (with audible alarm)
on-demand ramp up of capacity (6 mins)
subscriber alert push delivered
mass response traffic handled (followed by ramp down)

80. Architecture
Amazon Web Services provides services and
infrastructure to build reliable, fault-tolerant, and
highly available systems in the cloud.
These qualities have been designed into our services
both by handling such aspects without any special
action by you and by providing features that must be
used explicitly and correctly.

81. Managed Services Reduce:

82. Managed Services
Amazon Relational
Database Service
(RDS)
Amazon
ElastiCache
Amazon Simple
Queue Service
(SQS)
Elastic Load
Balancing
Amazon Elastic
MapReduce
Amazon Simple
Email Service
(SES)
Amazon Simple
Notification Service
(SNS)

83. $0.028
per hour
DNS
Elastic Load
Balancing
Web Servers
Availability Zone

84. $0.028
per hour
DNS
Elastic Load
Balancer
Web Servers
Availability Zone
VS
$0.08
per hour
(small instance)
DNS
EC2 instance
+ software LB
Web Servers
Availability Zone

85. Consumers
Producer
$0.50 per
1,000,000 Requests
($0.0000005 per Request)
SQS queue

86. Consumers
Producer
SQS queue
$0.50 per
1,000,000 Requests
($0.0000005 per Request)
VS
$0.08
per hour
(small instance)
Producer
EC2 instance
+ software queue
Consumers

87. Consolidated Billing

88. RI Purchases to grow a Resource Pool
35
30
25
E
D
C
B
A
20
15
Reserved Instance
Pool
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12

89. Tiered Pricing

90. Flexibility: Take advantage!
Architecture
vs.
Gardening
STOP/START
size changes
new instance types
vary capacity
rearrange, etc.

91. What are Spot Instances?
• Value
 Pricing
• Up to 92% discount
 Elastic
• Capacity not otherwise
available
 Minimum Commitment
• Commit to 1 hour
• Tradeoff
 Potential for interruption

92. Key Points about Spot
•
•
•
•
Spare capacity – supply and demand
Be prepared for no availability at times
Be willing to accept and deal with interruption
Far greater potential scale
starting at 5X default instance limits
• Massive possible capacity = new ideas…

93. Consider 2 Time-to-Value Scenarios
1) Value of results quickly diminishes
2) Value of result stable until deadline
e.g., Engineering simulations
e.g., Analytics before an M&A deal

94. Spot Applications
Ideal Applications
Batch Processing
Time-Delayable
Fault-Tolerant or Restartable
Compute-Intensive
Horizontally Scalable
Stateless Worker Nodes
Region and AZ Independent
Uses Deployment Automation
Less Ideal Applications
Interactive
Strict/Tight SLA for Completion
Expensive to Handle Terminations
Data-Intensive
In-Memory Scaling
Long-Running Worker Nodes
Requires a Single AZ
Manually Launched and Managed

95. Spot Advice and Tips
• Don’t build your reliability ENTIRELY on spot
vServ.mobi – exceptional and smart architecture
• With time flexibility, different approaches:
delayed results, lower cost
spend less, quicker answers
• Ask different questions:
with enormous capacity, what is now possible?

96. Look at the World Differently
•
•
•
•
•
•
Order of magnitude more capacity
New experiments enabled = innovation!
Lucky Oyster – recommendation exchange
Prototyping a new search technology idea (using Common Crawl)
3.4 billion web pages > 1 TB of data > Index of 400 million entities
“The cost? About $100... in about 14 hours”

97. A Spot Case Study
Ashay Padwal
CoFounder & CTO – vServ.mobi

99. GLOBAL
INNOVATION
FOCUSED
Award Winning
Mobile Ad Exchange
across Emerging Markets

100. 31 Bn Ad Requests / Month
11% EUROPE
11% REST OF ASIA
7% NORTH
AMERICA
33% INDIA
10% SOUTH
AMERICA
14% MIDDLE
EAST & AFRICA
14% SE ASIA
Over 200 Mn Unique Users / Month

101. Infrastructure: Requirements & Challenges
1
2
3
4
Requirement: Self Serve for Publisher On-boarding & Exit
Challenge: No Capacity Planning; Extreme Scalability
Requirement: Start Up
Challenge: No Capex, no Lock-in
Requirement: Least Latency & High Availability
Challenge: Suite of services – Compute, Load Balancing,
DNS, CDN, Storage, Multiple DCs per location
Requirement: Global Setup management with small team
Challenge: Availability across Regions with extensive APIs

102. Infrastructure: Solution
1
AWS
2
AWS
3
EC2 & ELB – Multi-AZ
Route53, CloudFront, S3
4
US East, US West, Europe, South America, Asia
For Middle East, we host in Turkey
For Africa, we host in South Africa

103. Deployment Overview

104. Ad Delivery Setup

105. Now What? Reduce Cost without impacting Performance
• AWS is pretty cost-effective. But we were greedy!
• Saving more meant more money for other areas in our
business.
• We walked in the opposite direction... and it worked!
• We use spot instances in production extensively.
• Sounds risky? - Yes, but if you architect your system
correctly, you should be safe.

106. What we did
1
2
Selected the right Instance Type
- use CloudWatch for CPU & memory usage
- Load Test
Designed our servers to be self-sufficient and perishable
-
3
Business logic & DB on same server
Transaction Logs written to EBS
Auto Setup on Server
Data Collection module
We built a custom Scaling solution
-
Add/Remove instances by checking present traffic & predicting traffic
in the immediate future
Based on trending of spot prices either try launching spot or fall back
to on-demand instances
Remove servers if in use between 45-55min
Track spot prices to shift to on-demand

107. What AWS did
1
Reduced pricing for EC2 (On Demand & Reserved) and S3
2
Cheap Archival System - Glacier
3
Pre warming of Load Balancer (ELB)
4
AMI movement across regions
5
ELB with equal distribution of traffic across instances
spread in any Availability Zone

108. THANK YOU!
Ashay Padwal
CTO & Co-Founder
ashay@vserv.mobi

109. Closing – Key Takeaways
• Re-evaluate, revist and re:Invent
Evolve along with AWS
• Leverage
Managed Services, CloudWatch
• Stay up to date
RI modifications, Trusted Advisor
• AWS Blog: aws.typepad.com

110. Please give us your feedback on this
presentation
CPN211
As a thank you, we will select prize
winners daily for completed surveys!