Cloud-Computing
Economies of Scale
AWS Executive Symposium
James Hamilton, 2009.11.10
VP & Distinguished Engineer
w: mvdirona.com/jrh/work
b: perspectives.mvdirona.com
Agenda
Infrastructure Efficiency at Scale
Cloud services really are different
Where does the money go?
Where does the power go?
Cloud Computing Economics
Why utility computing makes sense
economically
Amazon Web Services Specialization
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Economies of Scale
Large block h/w purchases significantly more economic
Large weekly purchases offer significant savings
H/W Manufacturers willing & able to do custom designs at scale
Automation & custom s/w investments amortize well at scale
Summary: scale economics strongly in play
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Large Service [$13/Mb/s/mth]: $0.04/GB
Medium [$95/Mb/s/mth]: $0.30/GB (7.1x)
2006 comparison of very large service with mid-size: (~1000 servers):
Large Service: $4.6/GB/year (2x in 2 Datacenters)
Medium: $26.00/GB/year* (5.7x)
Large Service: Over 1.000 servers/admin
Enterprise: ~140 servers/admin (7.1x)
Services Different from Enterprises
Enterprise Approach:
Largest cost is people – scales with servers (~100:1 common)
Enterprise interests center around consolidation & utilization
• Consolidate workload onto fewer, larger systems
• Large SANs for storage & large routers for networking
Internet-Scale Services Approach:
Largest costs is server & storage H/W
• Typically followed by cooling, power distribution, power
• Networking varies from very low to dominant depending upon service
• People costs under 10% & often under 5% (>1000+:1 server:admin)
Services interests center around work-done-per-$ (or joule)
Observations:
People costs shift from infrastructure to supporting the business
Expect high-scale service techniques to spread to enterprise
Focus instead on work done/$ & work done/joule
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Where Does the Money Go?Assumptions (not an Amazon facility):
Facility: ~$200M for 15MW facility, 82% is power dist & mech (15-year amort.)
Servers: ~$2k/each, roughly 50,000 (3-year amort.)
Average server power draw at 30% utilization: 80%
Server to Networking equipment ratio: 2.5:1 (“Cost of a Cloud” data)
Commercial Power: ~$0.07/kWhr
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Observations:• 62% per month in IT gear of which 44% in servers & storage
• Net gear costs high & only 38% in shell, power, power distribution, & mech.
Details at: http://perspectives.mvdirona.com/2008/11/28/CostOfPowerInLargeScaleDataCenters.aspx
& http://perspectives.mvdirona.com/2009/03/07/CostOfACloudResearchProblemsInDataCenterNetworks.aspx11/10/2009
44%
18%
19%
15%
4%Servers
Networking EquipmentPower Distribution & CoolingPower
Other Infrastructure
Monthly Costs
3yr server & 15 yr infrastructure amortization
PUE & DCiEMeasure of datacenter infrastructure efficiency
Power Usage Effectiveness
PUE = (Total Facility Power)/(IT Equipment Power)
Datacenter Infrastructure Efficiency
DCiE = (IT Equipment Power)/(Total Facility Power) * 100%
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http://www.thegreengrid.org/en/Global/Content/white-papers/The-Green-Grid-Data-Center-Power-Efficiency-Metrics-PUE-and-DCiE
Where Does the Power Go?
Assuming above average datacenter at PUE ~1.7
Each watt to server loses ~0.7W in power distribution & cooling
IT load (servers, storage, & networking): 1/1.7=> 59%
• Networking under 4%
Power losses easier to track than cooling:
Power transmission & switching losses: ~8%
Cooling losses remainder:100-(59+8) => 33%
Observations:
Server efficiency & utilization improvements highly leveraged
Cooling costs incredibly high at ~1/3
Net gear not large power consumer
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Agenda
Infrastructure Efficiency at Scale
Cloud services really are different
Where does the money go?
Where does the power go?
Cloud Computing Economics
Why utility computing makes sense
economically
Amazon Web Services Specialization
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Infrastructure at Scale
Datacenter design efficiency
Average datacenter efficiency low with PUE over 2.0 (Source: EPA)
• Many with PUE well over 3.0
High scale cloud services in the 1.2 to 1.5 range
Lower cost & much better for environment
Multiple datacenters
At scale multiple datacenters can be used
• Close to customer
• Cross datacenter data redundancy
• Address international markets efficiently
Avoid massive upfront data cost & years to utilize
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H/W Cost & Efficiency Optimization
Service optimized hardware
Custom cloud-scale design teams:
• Dell DCS, SGI (Rackable), ZT Systems, Verari, HP, …
Purchasing power at volume
Supply chain optimization
Shorter chain drives much higher server utilization
• Predicting next week easier than 4 to 6 months out
Less overbuy & less capacity risk
Networking transit costs rewards volume
Cloud services unblocks new business & growth
Remove dependence on precise capacity plan
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Investments at Scale
Deep automation only affordable when amortized
over large user base
Lack of automation drives both cost & human error fragility
S/W investments at scale
Massive distributed systems investments such as Amazon
Simple Storage Service & Elastic Block Store hard to justify
without scale
Special Skills with deep focus
Distributed systems engineers, power engineering,
mechanical engineering, server h/w design, networking,
supply chain, 24x7 operations staff, premium support,…
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Utilization & EconomicsServer utilization problem
30% utilization VERY good &10% quite common
• Expensive & not good for environment
Solution: pool number of heterogeneous services
• Single reserve capacity pool far more efficient
• Non-correlated peaks & law of large numbers
Pay as you go & pay as you grow model
Don’t block the business
Don’t over buy
Transfers capital expense to variable expense
Apply capital for business investments rather than infrastructure
Charge models drive good application owner behavior
Cost encourages prioritization of work by application developers
High scale needed to make a market for low priority work
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Agenda
Infrastructure Efficiency at Scale
Cloud services really are different
Where does the money go?
Where does the power go?
Cloud Computing Economics
Why utility computing makes sense
economically
Amazon Web Services Specialization
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Amazon Cycle of Innovation
15+ years of operational excellence
Managing secure, highly available, multi-datacenter
infrastructure
Experienced at low margin cycle of innovation:
Innovate
Listen to customers
Drive down costs & improve processes
Pass on value to customers
AWS announced price reduction October 27:
Up to 15% off all EC2 instance families & sizes
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AWS ApproachBroad set of services:
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“Open the hood” approach
Simple, layerable building block services
Component services are substitutable
Infrastructure Services
SimpleDB
Simple Storage Service
CloudFront
Simple Queue Service
Elastic MapReduce
Relational Database
Service
Elastic Block Store
Premium Support
Virtual Private Cloud
Payments & Billing
Flexible Payment
Services
DevPay
On Demand WorkforceMechanical Turk
Alexa Web ServicesWeb Information Service
Top Sites
Merchant ServicesFulfillment Web Service
AWS Scale
Looking at Quantcast Top 500k sites (11/2009)
53% of cloud hosted sites are using AWS
27% growth in AWS hosted site count Oct to Nov
More sites than all others combined
High growth workloads: data Intensive
computing, commercial HPC, analysis, &
optimization
Scale supports deep investment in automation,
monitoring, operations, & funds faster innovation
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Sources:
• http://www.quantcast.com/top-sites-1
• http://www.jackofallclouds.com/2009/11/state-of-the-cloud-november-2009/
AWS Pace of Innovation
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• RDS Announced
• High-Memory Instances
• Reduced EC2 Pricing
• AWS Multi-Factor Authentication
• Virtual Private Cloud
• Lower Reserved Instance Pricing
• AWS Security Center
• Reserved Instances in EU
• Elastic MapReduce
• SQS in EU
• New SimpleDB Features
• FPS General Availability
• Lower pricing tiers for CloudFront
• AWS Management Console
• EC2 Reserved Instances
• EC2 with Windows
• EC2 in EU
• AWS Toolkit for Eclipse
• AWS Import/Export
• New CloudFront Features
• Monitoring, Auto Scaling
& Elastic Load Balancing
• Elastic MapReduce in EU
• EBS Shared Snapshots
• SimpleDB in EU
• Monitoring in EU
• Auto Scaling in EU
• Elastic Load Balancing in EU
SummaryBenefit from AWS scale
Higher h/w & infrastructure utilization
Better environmentally
Achieve fundamental cost shift
Convert capital expense to variable cost
Lower operating costs at same time
Adapts to inaccurate capacity plans
Pay as you go & pay as you grow
Gain business agility
Obtain h/w resources for new project or expansion in minutes rather than months
Lower infrastructure cost/risk supporting more innovation
Higher productivity
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More InformationAmazon Web Services:• http://aws.amazon.com
Designing & Deploying Internet-Scale Services:http://mvdirona.com/jrh/talksAndPapers/JamesRH_Lisa.pdf
Where does the power go & what to do about it:• http://mvdirona.com/jrh/TalksAndPapers/JamesHamilton_AFCOM2009.pdf
Recovery-Oriented Computing:http://roc.cs.berkeley.edu/
http://www.cs.berkeley.edu/~pattrsn/talks/HPCAkeynote.ppt
http://www.sciam.com/article.cfm?articleID=000DAA41-3B4E-1EB7-BDC0809EC588EEDF
Autopilot: Automatic Datacenter Operation:http://research.microsoft.com/users/misard/papers/osr2007.pdf
Perspectives Blog:http://perspectives.mvdirona.com
Email:[email protected]
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