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Koomeyondatacenterelectricityuse v24

Koomeyondatacenterelectricityuse v24



This is a talk I gave at Green:Net in March 2009

This is a talk I gave at Green:Net in March 2009



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    Koomeyondatacenterelectricityuse v24 Koomeyondatacenterelectricityuse v24 Presentation Transcript

    • 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 1
    • For users, the cloud offers infinitely scalable computing on demand 2
    • So why should cloud users care about power use? 3
    • Power use strongly affects costs for “in-house” IT services (the alternative to relying on the cloud) AND 4
    • Cloud computing suppliers have two inherent advantages on power and costs over “in-house” IT (load diversity and economies of scale) 5
    • (As an aside, most people think the true total cost for “in-house” IT is far lower than it actually is) 6
    • Data centers, where the cloud resides, are where the world of bits meets the world of atoms 7
    • 8
    • The cloud uses electricity. How much? 9
    • World data center electricity use, 2000 and 2005 Source: Koomey 2008 10
    • How much is 152B kWh? Italy South Africa Mexico World Data Centers Iran Sweden Turkey 0 50 100 150 200 250 300 Final Electricity Consumption (Billion kWh) Source for country data in 2005: International Energy Agency, World Energy Balances (2007 edition) 11
    • Trends push power use both up and down 12
    • 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 13
    • Summary: Delivery of IT services is increasing rapidly 14
    • 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) 15
    • Summary: Information technology is becoming more energy efficient at a furious pace 16
    • Internet electricity intensity Electricity per GB transferred down 30% per year! Source: Taylor and Koomey (2008) for 2000 and 2006 data. Trends for 2000 to 2006 extrapolated to 2008 by JK. 17
    • Data center costs are strongly affected by IT power use 18
    • Annualized data center costs 19
    • Two important equations Power related terms 20
    • In spite of our historical progress, there’s still great potential for improving the energy efficiency of data centers 21
    • Many efficiency opportunities Source: EPA report to Congress 2007 22
    • Improving the energy efficiency of data centers is as much about people and institutions as it is about technology 23
    • 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) 24
    • 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 25
    • Cloud computing suppliers have at least two big advantages on power and costs over “in-house” IT 26
    • 1) Diversity: spread loads over many users, improving hardware utilization 27
    • 2) Economies of scale: implementing technical + organizational changes is cheaper and easier than for small IT shops 28
    • 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
    • Why? 30
    • Moving electrons is always less environmentally damaging than moving atoms 31
    • 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 32
    • 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. 33
    • 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/ 34
    • 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>. 35