Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically.  Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.

1. Stephen A. Elliott
Optimizing Your AWS Applications and Usage to Reduce Costs
Sr. Product Manager, EC2
Jason Stowe
CEO, Cycle Computing
Guest presenter:

2. Agenda
• Objective
– Review the spectrum of ways to save money on your AWS application
• Tenet: Fit the cloud to your product and business model
– Use Only What You Need (and pay only for what you use!)
– Measure and Manage
– Scale Opportunistically
• Customer Case Study
– Cycle Computing

3. Use Only What You Need
And pay only for what you use!

4. Scale on demand
Rigid On-Premise Resources
Waste
Customer
Dissatisfaction
Actual demand
Predicted Demand
Capacity
Time
Elastic Cloud Resources
Actual demand
Resources scaled to demand
Capacity
Time
VS.

5. Use only what you need: AWS cost savings opportunities
• Right-size your cloud resources
– Use resources that suit your needs (instance types, storage options, etc.)
– Improve performance: reduce churn, underutilization, bottlenecks
– Lower costs: maximize your output per dollar, don’t pay for performance you don’t
require
• Fit your payment model to your business model
– Do you value flexibility or predictability?
– Use a portfolio of payment models
• Measure and manage your application and cloud resources
– Monitor your applications to identify new savings opportunities

6. Right-size your cloud resources: broad EC2 selection
• An instance type for
every purpose
• Assess your memory
& CPU requirements
– Fit your application
to the resource
– Fit the resource to
your application
• Only use a larger
instance when
needed

7. Optimize your storage choice too: S3 & Glacier
• S3 and Glacier are both:
– Secure
– Flexible
– Low-cost
– Scalable: over 1.3 trillion customer objects
– Durable: 99.999999999% (11 “9”s)
Amazon
Glacier

8. Choosing between S3 and Glacier
• Amazon Simple Storage Service (S3)
– Designed to serve static content at high volumes, low latency, frequent access
– Low cost: as low as 5.5¢ per GB-month (or 3.7¢ for reduced redundancy)
• Amazon Glacier
– Designed for long-term cold storage: infrequent access, long retrieval times (3-5 hrs)
– Extremely low-cost: 1¢ per GB-month
• Tips:
– Optimize access: Reduce payload size, # of accesses (e.g., consolidated logs)
– Monitor for unexpected access/growth patterns: e.g., misconfigured log archiving
– Set Lifecycle Policies: object expiration dates; auto-move S3 files to Glacier
Illumina, the leading provider of DNA sequencing
instruments, uses Glacier to store large blocks of
genomic data all over the world

9. Fit your payment model to your business model: EC2 pricing plans
On-Demand
Instances
Reserved
Instances
Spot
Instances
Pay as you go for computing
power
Flat hourly rate, no up-front
commitments
Pay an up-front fee for a
capacity reservation and a lower
hourly rate (up to 72% savings)
1-year or 3-year terms
RI Marketplace: sell RIs you no
longer need; buy RIs at a
discount
Pay what you want for spare EC2
capacity: your instances run if
your bid exceeds the Spot price
Potential for large scale at low
cost: When they’re available,
take advantage of 1,000s of Spot
Instances at up to 90% savings
10:00
10:05
10:10
10:15

10. Use a spectrum of payment models
For example:
Frontend Applications
on On-Demand/Reserved Instances
+
Backend Applications*
on Spot Instances
* e.g., batch video transcoding

11. Reserved Instance Marketplace: Buy and Sell Your RIs
• Benefits for Buyers:
– Same underlying EC2 hardware
– Buy RIs at a discount from AWS price
– Increased selection of term lengths &
prices
• Benefits for Sellers:
– Moving to a new AWS region
– Changing your instance type
– Switching operating systems
– Selling capacity when project ends

12. Measure and Manage
“If you cannot measure it, you cannot improve it.”
- Lord Kelvin

13. Overview of AWS Monitoring and Management Services
• AWS provides detailed cloud monitoring and management
– Consolidated Billing (see “Account Activity” navigation panel)
– CloudWatch (see AWS Management Console)
– Billing Alerts (see “Account Activity” navigation panel)
– Trusted Advisor (see “Support Center”)
– Other APIs: tags, programmatic access, etc.
• Third-party services are also available

14. Consolidated Billing: Single payer for a group of accounts
• One Bill for multiple accounts
• Easy Tracking of account
charges (e.g., download CSV of
cost data)
• Group Activities by Paying
Account (e.g., Dev, Stage, Test,
Prod)
• Volume Discounts can be
reached faster with combined
usage
• Reserved Instances are shared
across accounts (including RDS
Reserved DBs)
• AWS Credits are combined to
minimize your bill

15. Consolidated Billing Demo (1/3)
• Get an overall summary total
for all your users and accounts

16. Consolidated Billing Demo (2/3)
• From your payment account
login, view details of each
linked account in one place

17. Consolidated Billing Demo (3/3)
• Drill down into detail’s of each
account
• Download a CSV file for line
item details, then analyze via
spreadsheet, pivot tables, etc.

18. Amazon CloudWatch
• Overview
– Monitoring for AWS cloud resources and applications
• AWS Resources: EC2, RDS, EBS, ELB, SQS, SNS, DynamoDB, EMR, Auto Scaling, …
• Custom metrics from your application (use Put API call)
– Gain insight, set alarms and notifications, react immediately
– Start using within minutes, auto-scale with your application
• Sophisticated Automation
– Use CloudWatch metrics with Auto Scaling to dynamically scale EC2 instances

19. Use CloudWatch to monitor & manage resource usage
• Monitor your resource utilization
– Are you using the right instance type?
– Have you left instances idle?
– Is your instance usage level or bursty?
• Manage your resource utilization
– Move bursty workloads to other instances
– Rebalance your worker nodes
– Scale nodes automatically with Auto Scaling

20. Use CloudWatch to create Billing Alerts
• Billing Alerts notify you when estimated charges reach a given threshold
• Use Billing Alerts to track an individual developer, or your whole business
• Easily set up your billing alarm and actions

21. Trusted Advisor: Enterprise Strength Monitoring/Optimization
• Monitors and recommends
optimizations for:
– Cost
– Security
– Fault Tolerance
– Performance
• Available to customers with
Business and Enterprise-
level support
http://aws.amazon.com/premiumsupport/trustedadvisor/

22. Trusted Advisor: Cost Optimization Tips

23. Trusted Advisor: Performance Tips

24. Third-party services to optimize your AWS usage

25. Scale Opportunistically
Opportunity favors the prepared application

26. Time-to-Result Case 1: Value of result quickly diminishes
Example:
Engineering
simulation
Delay  Loss of
productivity,
project slips

27. Time-to-Result Case 2: Result is valuable…until it’s not
Example:
Weekend
regression tests
Delay  Minimal
impact until
8:00AM Monday

28. Consider Spot Instances for greater savings and scale
• Spot in a nutshell
– Spot instances run when Your Bid ≥ Spot Price
– Spot instances = Spare EC2 instances
– Spot instances might be interrupted at any time
• Benefits
– Savings: Up to 90% off On-Demand
– Scale: Access up to 1,000s of EC2 instances
• To use Spot
– Decide on a bid price
– Launch via Console, API, Auto Scaling
– Monitor Bid Statuses via Console/API

29. What applications work on Spot?
• Good Spot applications are:
– Delayable: to balance SLA/cost
– Scalable: “embarrassingly parallel”
– Fault-tolerant: can be terminated without losing all work
– Portable across regions, AZs, instance types
• Examples:
– MapReduce (Hadoop, Amazon EMR)
– Scientific Computing (Monte Carlo simulations)
– Batch Processing (video transcoding)
– Financial Computing (high-frequency trading algorithm backtesting)
– and many others…
Lucky Oyster crawled 3.4B Web Pages,
building a 400M entry index in around
14 hours for $100 (>85% savings)!

30. • Auto Scaling auto-sizes your cluster based on preset triggers and schedules
• Integrates with CloudWatch metrics
• Use Auto Scaling to
– Improve customer experience, application performance
– Maximize CPU/IO/Memory utilization
– Optimize other metrics
Use Auto Scaling to dynamically scale your app
Scale with Real-Time Demand

31. Auto-Scaling Example: Netflix

32. Follow the Money vs. Follow the Customer
• Optimize utilization
– Auto Scale on utilization metrics: CPU, memory, requests, connections, …
• Optimize price paid
– Scale with Spot instances when Spot prices are low
– e.g., Run batch processes off-peak (nights, weekends) when Spot prices are lower

33. Follow the Money vs. Follow the Customer
• Optimize customer experience with Auto Scaling
• Example 1: Scale resources to meet customer demand
– Video service Auto Scales instances to respond to customer web service requests
• Example 2: Scale resources to ensure fresh results
– A scientific paper search engine Auto Scales on queue depth (# of new docs to crawl)
– 10 instances steady state and up to 5,000+ to ensure minimum throughput time
• Example 3: Scale resources preemptively before large demand
– A TV show marketing site scales up before the show and back down after

34. Cost-Saving Examples
• Achieve potentially
large savings by
profiling your
application and
paying only for
what you need
Base Case Savings Examples
You run 10 m3.2xlarge’s
On-Demand 24x7:
10 instances
X $1.00/inst-hours
X 24 hours/day
X ~30.5 days/month
= $7,320/month
If you need to run 100% of the time, indefinitely:
10x 3-yr Heavy RIs @ 100% Utilization
= $2,731/month (63% savings)
If you can layer RIs and On Demand to meet demand:
4x 3-yr Heavy RIs @ 100% Utilization
4x 3-yr Light RIs @ 15% Utilization
2x On-Demand @ 5% Utilization
= $1,843/month (75% savings)
If you Auto Scale from 2 to 10 instances around
primetime TV (6-11pm, Mon-Fri):
2x 3-yr Heavy RIs @ 100% Utilization
8x 3-yr Light RIs @ 15% Utilization
= $1,683/month (77% savings)
If you can use 40x Spot Instances at 25% up-time:
= $840/month (89% savings)

35. Customer Example:
39 Core-Years of Science
Cycle Computing’s 10,600-Instance Run
Jason A. Stowe
CEO
@jasonstowe, @cyclecomputing

36. At Cycle, we believe
innovation is shackled
by a lack of access
to compute and data

37. The Scientific Method
Ask a
Question
Hypothesize Predict
Experiment /
Test
Analyze Final Results
Test and Analyze stages
require the most time,
compute, and data

38. The Scientific Method
Ask a
Question
Hypothesize Predict
Experiment /
Test
Analyze Final Results
Any improvements to this
cycle yield multiplicative
benefits

39. If we democratize access to
high performance compute,
we’ll accelerate breakthroughs

40. Utility HPC in the News
WSJ, NYTimes, Wired, Bio-IT World BusinessWeek

41. Computing access is a challenge across many industries
All areas of scientific, engineering & financial research need affordable compute
• Cycle’s session at AWS Re:Invent
– Johnson & Johnson, Novartis, Life Technologies, Pacific Life Insurance, Hartford
Insurance Group talking about AWS, Cycle & Utility HPC
• Other clients that need affordable infrastructure
– 2 of Big 3 Finance
– 6 of Big 8 Pharmaceutical
– 3 of Big 5 Life Insurance
– 2 of Big 3 Next Generation Sequencing
– Start-ups to Fortune 100s, corporations to public research institutions

42. Cycle’s software orchestrates High Performance Computing,
Spot Instances drive down infrastructure costs
How does Spot help us do this?
Start-up
12.5 compute-years
in 3 hours on 50,000
cores ($20Million)
for < $3,000
Big 10 Pharma
154,000 simulations
on 30,000 cores in 9
hours for $10,000
Identified 3 new
leads in wet lab
Research Institute:
1 million hours or
115 compute years,
in 1 week
for $19,555

43. Cycle’s view of this cluster:
Big 10 Pharma Created
10,600 instance cluster
($44M) in 2 hours,
running
39 years of compute
in 11 hours for $4,372
Most Recent Utility Supercomputer
server count:
AWS Console view:

44. Utility HPC / BigData Orchestration Software
• Scale clusters from
50–50,000 cores
• Error-handling, reliability
• Data scheduling:
internal  cloud
• Automated Security,
Encryption Framework
• Audit, compliance
• Reporting, chargeback
Automate spot bidding =>

45. Shared FS
Scale from 50–50,000+ cores
CycleCloud Deploys Secured, Auto-scaled HPC Clusters
User
Check job load
Calculate ideal HPC cluster
Legacy
Internal
HPC
Load-based Spot bidding
Properly price the bids
Manage Spot Instance loss
Spot Instance Execute Nodes
(auto-started & auto-stopped)
FS /
S3
HPC Cluster
On-Demand Execute Nodes
Data Scheduling to place data
HPC Orchestration to
Handle Spot Instance Bid & Loss

46. THANK YOU!
Email: jason.stowe@cyclecomputing.com
Twitter: @jasonastowe, @cyclecomputing
Blog: blog.cyclecomputing.com

47. Conclusion (Part I):
Fit the cloud to your product and business model
• Use Only What You Need (and pay only for what you use!)
• Measure and Manage
• Scale Opportunistically

48. An example putting it all together: Saving on Batch Processing
http://aws.amazon.com/architecture/
3. Scale
Opportunistically:
Auto Scale worker
nodes based on size
of input queue1. Pay Only
for What You
Use: Right-
size your
cloud
resources
2. Monitor and
Manage your system
with CloudWatch,
Billing Alerts, Trusted
Advisor

49. Conclusion (Part II):
Use the cloud to create new products & business models
On-Premises
• Failure is
expensive
• Experiment
infrequently
• Less Innovation
Optimized Cloud
• Failure is
inexpensive
• Experiment early
and often
• More Innovation

50. THANK YOU

51. APPENDICES

52. Other simple optimization tips
• Don’t forget to…
– Disassociate unused EIPs
– Delete unassociated Amazon EBS volumes
– Delete older Amazon EBS snapshots
– Leverage Amazon S3 Object Expiration
– Defer batch activity (e.g., Hadoop) to periods
when your RIs are regularly underutilized
(For Enterprise-level support, Trusted Advisor can
help with some of these.)
• Netflix’s Janitor Monkey automates clean-up
– Reduces “unintentional” resource usage
– Reduces cost and clutter

53. Other Spot Instance Use Cases
• Batch Processing: Generic batch processing (scale out computing)
• Hadoop: MapReduce processing (e.g., Search, Big Data)
• Scientific Computing: Scientific trials, simulations, analysis
• Video/Image Processing: Encoding, transcoding, rendering
• Testing: Continuous testing, load testing websites, etc.
• Web/Data Crawling: Analyzing data and processing it
• Financial: Hedge fund analytics, energy trading, etc.
• HPC/HTC: Embarrassingly parallel jobs
• Cheap Compute: Backend servers for Facebook games, MineCraft

54. Steady State
Example: Corporate Website
Spiky Predictable
Example: Marketing
Promotions Website
Uncertain unpredictable
Example: Social game or
Mobile Website
Application Usage Patterns

55. Amazon Elastic MapReduce
Hadoop Cluster
HDFS
Task
Node
Task
Node
Core
Node
Core
Node
Input
Data Output
Data
Amazon S3
Metadata
Amazon SimpleDB
BI Apps
Upload large datasets or
log files directly
Data
Source
Code/
Scripts
Amazon S3
Service
Amazon Elastic
MapReduce
HiveQL
Pig Latin
Cascading
Mapper
Reducer
Runs multiple
JobFlow Steps
Name
Node
JDBC/ODBC
HiveQL
Pig Latin
Query
Amazon EMR (Hadoop): Run Task Nodes on Spot

56. Paying as you go on AWS lowers your Total Cost of Ownership
• By paying only for what you use,
you can save on:
– Servers
– Storage
– Network
– Environment
– Administration
• Example: 82% TCO savings for
Thomsen Reuters
• Learn more:
aws.amazon.com/economics

57. Example Spot Customers

58. Example Architecture 2: Web Application Hosting
http://aws.amazon.com/architecture/