Journey Through the AWS Cloud; Cost Optimisation

Journey through the Cloud:
Cost optimization
Ryan Shuttleworth – Technical Evangelist
@ryanAWS

Journey through the cloud

Common use cases & stepping stones into the AWS cloud
Learning from customer journeys
Best practices to bootstrap your projects

Cost Optimization

A key step in the cloud journey
Realize cost aware architectures
Use elasticity to real and measurable benefit
Do more, use less

Agenda
Fundamentals of AWS cost optimization
Cost optimization in 5 steps
Where to go next

Fundamentals of cost optimization

Why optimize?

Utility
Compute and Storage
are a utility so ‘turning
off’ should be natural

Why optimize?

Utility Efficiency
Compute and Storage Efficiency allows
are a utility so ‘turning more to be done
off’ should be natural within a given budget

Why optimize?

Utility Efficiency Architecture
Compute and Storage Efficiency allows Cost awareness drives
are a utility so ‘turning more to be done adoption of 21st century
off’ should be natural within a given budget architectures

Turn off the lights
When you stop EC2 resources you stop
paying for them

Be elastic
Support workloads with the right
amount of horsepower to get the job
done

Continually optimize
Drive recurring and improving savings
through cost aware architectures

5 Steps for cost
optimization
Elastic capacity
Instance types
Reserved instances
Spot instances
Complementary services

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

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

Traditional capacity required
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

Server Load


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

Server Load


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


Server Load


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

Server Load


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

Time: +00h

<10 cores

Elastic Capacity

Time: +24h
>1500 cores

Elastic Capacity

Time: +72h

<10 cores

Elastic Capacity

Time: +120h

>600 cores

Elastic Capacity

Auto-scaling policies

Manually By Schedule
Send an API call or use CLI to Scale up/down based on date
launch/terminate instances – and time
Only need to specify capacity
change (+/-)

By Policy Auto-Rebalance
Scale in response to changing Instances are automatically
conditions, based on user launched/terminated to
configured real-time ensure the application is
monitoring and alerts balanced across multiple Azs

Auto-scaling policies

Scaling base on Policy Scaling by Schedule
Scale up and down base on metrics Scheduled Actions to meet known
demand
Scaling Up policy - Double the group
size if avg cpu > 80% Scheduled up to 31 days into the future
Scaling Down policy - Decrement by Recurring scheduled scaling activities
10% if avg cpu < 30%

6

5
Instance Count

4

3

2

1

0
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Day of Month

6

5
Monthly
Instance Count

4
predictable
3 peak
processing
2

1

0
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Day of Month

6

5
Instance Count

4

3

2

1

0
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Day of Month

6

5
Instance Count

4

3

2

1
Elastic Capacity
0
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Day of Month

6

5
Instance Count

4
75 % Savings
3

2

1
Elastic Capacity
0
0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Day of Month

Instance types

Start
Choose instance that
meets your basic
requirements best
Match memory & virtual
cores

Instance types

Start Tune
Choose instance that Change instance size up
meets your basic or down based upon
requirements best monitoring
Match memory & virtual Use trusted advisor to
cores assess

Instance types

Start Tune Spread
Choose instance that Change instance size up Run instances across
meets your basic or down based upon multiple availability
requirements best monitoring zones
Match memory & virtual Use trusted advisor to Smaller sizes equals
cores assess greater granularity

Know your usage

Free Memory
Free CPU
Free HDD
PUT 2 weeks
…
Custom Metrics
…
Amazon
Instance At 1-min intervals Alarm
CloudWatch

Choose your metric
optimize for the metric

Choose your metric
Cost per unit of work per instance(size)

Workload A Workload B Workload C

Optimal on 4x Optimal on 10x Optimal on 2x
m1.xlarge m1.medium m3.xxlarge

Choose your 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

Choose your metric

Think workload density
Don’t focus on instance hourly rate per se

Master Account
aws.invoices@mycompany.com

Master Account

consolidated billing information

Division B
admin@divisionB.com
IAM User2
Dev2
Admin2

Master Account

consolidated billing information Tags: (key-
value)
Division B e.g Own=Div
Proj=R
admin@divisionB.com
IAM User2
Dev2
Admin2

Tags: Tags: Tags:
Own=Div Own=Div Own=Div
Proj=P Proj=Q Proj=R

Master Account


Operating Co. A Division B Business Unit C
admin@opcoa.com admin@divisionB.com admin@busUnitC.com
User1 User2 User3
IAM

IAM

IAM
Dev1 Dev2 Dev3
Admin1 Admin2 Admin3

Tags: Tags: Tags: Tags: Tags: Tags: Tags: Tags: Tags:
Own=OpCo Own=OpCo Own=OpCo Own=Div Own=Div Own=Div Own=BusC Own=BusC Own=BusC
Proj=A Proj=B Proj=C Proj=P Proj=Q Proj=R Proj=X Proj=Y Proj=Z

Programmatic billing access
Master Account

S3 CSV


Operating Co. A Division B Business Unit C
admin@opcoa.com admin@divisionB.com admin@busUnitC.com
User1 User2 User3
IAM

IAM

IAM
Dev1 Dev2 Dev3
Admin1 Admin2 Admin3

Tags: Tags: Tags: Tags: Tags: Tags: Tags: Tags: Tags:
Own=OpCo Own=OpCo Own=OpCo Own=Div Own=Div Own=Div Own=BusC Own=BusC Own=BusC
Proj=A Proj=B Proj=C Proj=P Proj=Q Proj=R Proj=X Proj=Y Proj=Z

Basic Offering
24x7x365 ✓
Developer Forum Access ✓
Documentation ✓
Business Access to support Phone, Chat,
Email
Enterprise Named Contacts 5
Fastest Response Time 1 Hour

Architecture Support Use Case
Guidance

Best Practice ✓
Diagnostics Tools ✓
Direct Routing ✓
3rd Party Software ✓
Trusted Advisor ✓

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

Reserved instances

On-demand instances Reserved instances

Unix/Linux instances start at 1- or 3-year terms
$0.02/hour
Pay low up-front fee, receive significant hourly
Pay as you go for compute power discount

Low cost and flexibility Low Cost / Predictability

Pay only for what you use, no up-front Helps ensure compute capacity is available
commitments or long-term contracts when needed

Use Cases:
Use Cases:
unpredictable workloads; Applications with steady state or predictable
usage
Applications that require reserved capacity,
including disaster recovery

Reserved instances Heavy utilization RI

> 80% utilization
Lower costs up to 58%
On-demand instances Reserved instances Use Cases: Databases, Large Scale HPC,
Always-on infrastructure, Baseline

$0.02/hour



Use Cases:
Use Cases:
usage


> 80% utilization

$0.02/hour
Pay low up-front fee, receive significant hourly Medium utilization RI

Low cost and flexibility Low Cost / Predictability 41-79% utilization
Pay only for what you use, no up-front Helps ensure compute capacity is available Use Cases: Web applications, many heavy
commitments or long-term contracts when needed processing tasks, running much of the time

Use Cases:
Use Cases:
usage


> 80% utilization

$0.02/hour
Pay low up-front fee, receive significant hourly Medium utilization RI

Low cost and flexibility Low Cost / Predictability 41-79% utilization
Pay only for what you use, no up-front Helps ensure compute capacity is available Use Cases: Web applications, many heavy
commitments or long-term contracts when needed processing tasks, running much of the time

Use Cases:
Use Cases:
Applications with short term, spiky, or Light utilization RI
usage
Application development or testing 15-40% utilization
Applications that require reserved capacity, Lower costs up to 34%
Use Cases: Disaster Recovery, Weekly /
Monthly reporting, Elastic Map Reduce

Best RI for Utilisation
$18,000

$16,000

$14,000

$12,000

$10,000
Heavy
Medium
$8,000
Light
$6,000 O-Demand

$4,000

$2,000

$-

Optimizing costs with RIs
14

12

10

On Demand
8
Light Utilization RI

6 Medium Utilization RI
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

Spot instances

On-demand instances Reserved instances

$0.02/hour



Use Cases:
Use Cases:
usage

Spot instances

On-demand instances Reserved instances Spot instances

Unix/Linux instances start at 1- or 3-year terms Bid on unused EC2 capacity
$0.02/hour
Pay low up-front fee, receive significant hourly Spot Price based on supply/demand,
Pay as you go for compute power discount determined automatically

Low cost and flexibility Low Cost / Predictability Cost / Large Scale, dynamic workload handling

Use Cases:
Use Cases:
Use Cases: Applications with flexible start and end times
unpredictable workloads; Applications with steady state or predictable Applications only feasible at very low compute
usage prices

Achieving economies of scale
100%

Time

100%

Reserved capacity

Time

100%

On

On-demand

Reserved capacity

Time

100%
Spot
On

On-demand

Reserved capacity

Time

If your bid > spot price
You get an instance

If your bid < spot price
Your instance is terminated

Architecting for spot instances

Decouple components Design for interruption
Separate interactive & backend Use SQS, SWF
processing
Place data in a durable store such as S3,
Use frameworks such as Elastic SimpleDB or DynamoDB
MapReduce
Save progress regularly

EMR with spot instances

Scenario #1

Job Flow

Duration:
14 Hours

#1: Cost without Spot
4 instances *14 hrs * $0.50 = $28


Scenario #1 Scenario #2

Job Flow Job Flow

Duration: Duration:
14 Hours 7 Hours

#1: Cost without Spot
4 instances *14 hrs * $0.50 = $28



Job Flow Job Flow

Duration: Duration:
14 Hours 7 Hours

#1: Cost without Spot #2: Cost with Spot
4 instances *14 hrs * $0.50 = $28 4 instances *7 hrs * $0.50 = $14 +
5 instances * 7 hrs * $0.25 = $8.75
Total = $22.75



Job Flow Job Flow

Time Savings: 50%
Duration: Cost Savings: ~22% Duration:
14 Hours 7 Hours

#1: Cost without Spot #2: Cost with Spot
4 instances *14 hrs * $0.50 = $28 4 instances *7 hrs * $0.50 = $14 +
5 instances * 7 hrs * $0.25 = $8.75
Total = $22.75

Spot market
Bidding strategies

Spot bidding strategies

Bid Distribution (for 3 months period)
20%
Percentage of the Distribution

18%
16%
14%
12%
10%
8%
6%
4%
2%
0%

Bid Price as Percentage of the On-Demand Price


20%

18%
16%
14%
12%
Bid near the RI
10%
8%
hourly price
6%
4%
2%
0%



20%

18%
16%
14%
12%
Bid above the spot price
10%
8%
history
6%
4%
2%
0%



20%

18%
16%
14%
12% Bid near the
10% on-demand
8% price
6%
4%
2%
0%



20%

18%
16%
14%
12%
10% Bid above the on-demand price
8%
6%
4%
2%
0%


Bid near the reserved hourly price
You only pay for a full hour

Bid near the reserved hourly price
You only pay for a full hour
(if you are interrupted the hour is free)

Bid above the on-demand price
Expect fewer interruptions

Bid above the on-demand price
Expect fewer interruptions
(only pay the spot price)

Implement cost aware architecture
Flip from spot to on-demand as price dictates

$0.028
per hour DNS Elastic Load Web Servers
Balancer
Availability Zone

$0.028
per hour DNS Elastic Load Web Servers
Balancer
Availability Zone

VS

$0.085
per hour EC2 instance
(small instance) DNS Web Servers
+ software LB
Availability Zone

Consumers
Producer SQS queue

$0.01 per
10,000 Requests
($0.000001 per Request)

Consumers
Producer SQS queue

$0.01 per
10,000 Requests
($0.000001 per Request)

VS

$0.085
per hour Producer
(small instance) EC2 instance Consumers
+ software queue

Software vs services

Software on EC2 AWS Services
Pros: ELB, SNS, SQS, SES, SWF, DynamoDB etc
Use custom features
Pros:
Pay as you go
Cons:
Scalability
Requires an instance
Availability
SPOF
High performance
Limited to one AZ
DIY administration

Useful links

aws.amazon.com/economics
aws.amazon.com/calculator

Useful links

http://aws.amazon.com/whitepapers

Journey Through the AWS Cloud; Cost Optimisation

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