koomeyondatacenterelectricityuse v24
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The Environmental Cost of Cloud Computing: Assessing Power Use
and Impacts Jonathan G. Koomey, Ph.D.
http://www.koomey.com Lawrence Berkeley National Laboratory &
Stanford University Presented at Green:Net
San Francisco, CA March 24, 2009
Power use strongly affects costs for “in-house” IT
services (the alternative to relying on the cloud) AND
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Cloud computing suppliers have two inherent
advantages on power and costs over “in-house” IT
(load diversity and economies of scale)
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(As an aside, most people think the true total cost for “in-house” IT is far lower
than it actually is)
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How much is 152B kWh?
Source for country data in 2005: International Energy Agency, World Energy Balances (2007 edition)
Turkey
Sweden
Iran
World Data Centers
Mexico
South Africa
Italy
Final Electricity Consumption (Billion kWh) 0 50 100 150 200 250 300
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Pushing power use up… • Increasing demands for
– E-commerce – VOIP – Internet search – software as a service – video downloads – resilience in the face of disaster – regulatory compliance (e.g. Sarbanes-Oxley) – IT-enabled business transformation
• More transistors on a chip + more RAM + more volume servers
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Pushing power use down…
• Virtualization/consolidation • Cooling and power constraints • Recognition of constraints by the C level • Metrics
– Servers + other IT equipment (Spec Power, 80 plus, E*)
– Site infrastructure • Utility rebates (PG&E)
Internet electricity intensity
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Source: Taylor and Koomey (2008) for 2000 and 2006 data. Trends for 2000 to 2006 extrapolated to 2008 by JK.
Electricity per GB transferred down 30% per year!
In spite of our historical progress, there’s still great potential for improving the energy efficiency of data
centers
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Improving the energy efficiency of data centers is as
much about people and institutions as it is about
technology
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Efficiency opportunities
• Improve asset management and utilization (multiple benefits)
• Improve efficiency of components (e.g. power supplies)
• Implement consistent metrics and track over time
• Align incentives to minimize True Cost of Ownership
• Think “whole system redesign” (RMI)
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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
2) Economies of scale: implementing technical + organizational changes is
cheaper and easier than for small IT shops
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The biggest environmental story about information
technology (IT) is not direct electricity use (which is
relatively small) but how IT affects efficiency in the
broader society 29
Example: paper vs. PDF
• Mass of paper = 5 g/sheet • Mass of electrons to move a 1 MB PDF
file of that page (based on average network electricity intensity of 7 kWh/GB) is 1.7 x 10-5 g
• Ratio of paper mass to electron mass ~ 300,000
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Conclusions • The cloud is responsible for 1-2% of the world’s
electricity use. • Absolute electricity use growing fast (doubling
every 5-8 years) • IT services are growing much faster than
electricity use (doubling every year or two). • Electricity productivity, defined as computing
services delivered per kWh, is increasing rapidly and this trend promises to continue.
• The indirect environmental and productivity benefits of IT are likely to be more important than direct electricity use.
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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.upsite.com/
TUIpages/tuihome.html • 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.upsite.com/cgi-bin/admin/admin.pl?admin=view_whitepapers)
• 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 >.
• 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>.
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