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Koomeyoncloudcomputing V5

Koomeyoncloudcomputing V5






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    Koomeyoncloudcomputing V5 Koomeyoncloudcomputing V5 Presentation Transcript

    • 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 1
    • Your choice: Current in-house IT or cloud computing? 2
    • 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.) 3
    • My claim: Powerful economic trends will push users more and more towards cloud computing 4
    • One of the main drivers of those trends is more efficient power use by cloud computing providers 5
    • Data center costs are strongly affected by IT power use, particularly server power 6
    • 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 7
    • Two important equations Power related terms 8
    • Current server W/k$ ∑: 25 to 100 Watts/k$ is the Numbers current range next to points represent watts/ thousand 2009 dollars. Source: Koomey et al. 2009a. 9
    • Some anecdotal data on Watts/k$ over time ∑: Watts/k$ doubled every 4-5 years in the past decade See Koomey et al. 2009a for details. 10
    • Annualized data center costs x2 Source: Koomey et al. 2009a 11
    • Improving the energy efficiency of data centers is as much about people and institutions as it is about technology 12
    • Corporate Average Datacenter Efficiency (CADE) Facility IT Domain Domain Q4 Q1 Facility IT Asset CapEx Asset Utilization Utilization Facility IT Energy Energy, Energy Efficiency CO2 OpEx Efficiency Q3 Q2 © 2008, 2010 Uptime Institute 13
    • 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) 14
    • 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 15
    • Cloud computing suppliers have at least four inherent advantages on power and costs over “in-house” IT. 16
    • 1) Diversity: spread loads over many users, improving hardware utilization 17
    • 2) Economies of scale: implementing technical + organizational changes is cheaper per computation than for small IT shops 18
    • 3) Flexibility: management of virtual servers easier and cheaper than physical servers 19
    • 4) Enabling structural change: Often easier to shift to cloud providers than to fix institutional problems in internal IT organizations 20
    • Carbon taxes will accelerate these trends (and accentuate regional differences in sources of power generation) 21
    • Maximum effect of $19/t CO2 price on data center costs 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). 22 CO2 tax = 2 ¢/kWh delivered; electricity price = 6.9 ¢/kWh (2009 $).
    • Big picture: Better to move bits than atoms CO2 emissions for downloads and physical CDs Physical CDs Digital downloads 23 Source: Weber et al. 2009
    • 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 24
    • 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/ 25
    • 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>. 26
    • 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> 27