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Power-related advantages of cloud computing

Jonathan G. Koomey, Ph.D. http://www.koomey.com

Project Scientist, LBNL and Consulting Professor, Stanford University

May 17, 2010

Your choice: Current in-house IT

or cloud computing?

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Cloud computing

•  Users pay for computing cycles and don’t worry about the back end

•  Manage virtual servers—characteristics of physical servers less important

•  Can be internal (replacing standard data centers) or external (e.g., Google, Microsoft, Amazon etc.)

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My claim: Powerful economic trends will

push users more and more towards cloud computing

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One of the main drivers of those trends is more efficient

power use by cloud computing providers

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Data center costs are strongly affected by IT power use, particularly server power

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Key definitions

•  ATC = Annualized total costs •  IT = Capital cost of IT equipment •  INFkw = Power-related infrastructure capital

costs •  INFnon-kW = Non-power-related infrastructure

capital costs •  EC = Energy costs •  O&M = Operation and maintenance costs

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Two important equations

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Power related terms

Current server W/k$

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Numbers next to points represent watts/thousand 2009 dollars. Source: Koomey et al. 2009a.

∑: 25 to 100 Watts/k$ is the current range

Some anecdotal data on Watts/k$ over time

10 See Koomey et al. 2009a for details.

∑: Watts/k$ doubled every 4-5 years in the past decade

Annualized data center costs

11 Source: Koomey et al. 2009a

x 2

Improving the energy efficiency of data centers is as

much about people and institutions as it is about

technology

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13 © 2008, 2010 Uptime Institute

Corporate Average Datacenter Efficiency (CADE)

IT Asset Utilization

Facility Energy

Efficiency

IT Energy Efficiency

IT Domain Facility Domain

Facility Asset

Utilization

Energy, CO2 OpEx

CapEx Q1

Q2

Q4

Q3

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Efficiency opportunities

•  Think “whole system redesign” (RMI) •  Align incentives to minimize True TCO •  Implement consistent metrics and track

over time •  Improve asset management and

utilization (multiple benefits) •  Improve efficiency of systems (e.g.

cooling) and components (e.g. power supplies)

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Misplaced incentives •  Energy, efficiency, and performance metrics

not standardized •  Not charging per kW but per square foot •  Split accountability

–  Who pays the bills, IT or facilities? –  Who bears the risk of failure?

•  Hierarchy and culture differences •  Piling safety factor upon safety factor •  Not focusing on total costs for delivering

computing services

Cloud computing suppliers have at least four inherent advantages on power and costs over “in-house” IT.

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1) Diversity: spread loads over many users,

improving hardware utilization

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2) Economies of scale: implementing technical + organizational changes is cheaper per computation

than for small IT shops

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3) Flexibility: management of virtual servers easier and

cheaper than physical servers

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4) Enabling structural change: Often easier to shift to cloud providers than to fix institutional problems in internal IT

organizations

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Carbon taxes will accelerate these trends (and accentuate

regional differences in sources of power generation)

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Maximum effect of $19/t CO2 price on data center costs

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CO2 tax

Assumes coal-fired power generation and CO2 tax of $19/t CO2 (comparable to the current price in the European emissions trading system). CO2 tax = 2 ¢/kWh delivered; electricity price = 6.9 ¢/kWh (2009 $).

Big picture: Better to move bits than atoms

Source: Weber et al. 2009

Physical CDs Digital downloads

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CO2 emissions for downloads and physical CDs

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Conclusions •  Cloud computing’s inherent cost advantages

will continue to drive customers to use it •  Power efficiency is one of the main sources of

these advantages (and pricing carbon will make the case more compelling)

•  Conventional internal data centers will still be important for certain kinds of applications, but will diminish in importance over time

•  Issues about liability, property rights, and security in the cloud will need to be sorted out, but the economic benefits will create pressure to do just that

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Key web sites

•  EPA on data centers + 2007 Report to Congress http://www.energystar.gov/datacenters

•  LBNL on data centers: http://hightech.lbl.gov/datacenters.html

•  The Green Grid: http://www.thegreengrid.org/ •  The Uptime Institute: http://www.uptimeinstitute.org

•  SPEC power: http://www.spec.org/power_ssj2008/

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References •  Koomey, Jonathan. 2007a. Estimating regional power consumption by

servers: A technical note. Oakland, CA: Analytics Press. December 5. <http://www.amd.com/koomey>

•  Koomey, Jonathan. 2007b. Estimating total power consumption by servers in the U.S. and the world. Oakland, CA: Analytics Press. February 15. <http://enterprise.amd.com/us-en/AMD-Business/Technology-Home/Power-Management.aspx>

•  Koomey, Jonathan, Kenneth G. Brill, W. Pitt Turner, John R. Stanley, and Bruce Taylor. 2007. A simple model for determining true total cost of ownership for data centers. Santa Fe, NM: The Uptime Institute. September. <http://www.uptimeinstitute.org/>

•  Koomey, Jonathan. 2008. "Worldwide electricity used in data centers." Environmental Research Letters. vol. 3, no. 034008. September 23. <http://stacks.iop.org/1748-9326/3/034008>.

References (continued) •  Koomey, Jonathan G., Christian Belady, Michael Patterson, Anthony Santos,

and Klaus-Dieter Lange. 2009a. Assessing trends over time in performance, costs, and energy use for servers. Oakland, CA: Analytics Press. August 17. <http://www.intel.com/pressroom/kits/ecotech>

•  Koomey, Jonathan G., Stephen Berard, Marla Sanchez, and Henry Wong. 2009b. Assessing trends in the electrical efficiency of computation over time. Oakland, CA: Analytics Press. August 17. <http://www.intel.com/pressroom/kits/ecotech>

•  Stanley, John, and Jonathan Koomey. 2009. The Science of Measurement: Improving Data Center Performance with Continuous Monitoring and Measurement of Site Infrastructure. Oakland, CA: Analytics Press. October 23. <http://www.analyticspress.com/scienceofmeasurement.html>

•  Taylor, Cody, and Jonathan Koomey. 2008. Estimating energy use and greenhouse gas emissions of Internet advertising. Working paper for IMC2. February 14. <http://imc2.com/Documents/CarbonEmissions.pdf>.

•  Weber, Christopher, Jonathan G. Koomey, and Scott Matthews. 2009. The Energy and Climate Change Impacts of Different Music Delivery Methods. Analytics Press. August 17. <http://www.intel.com/pressroom/kits/ecotech>

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