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Revolutionizing Data Center Efficiency

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A presentation from McKinsey & Company shows how data center facilities and electricity costs are growing at 10-15% a year with many organizations pursuing data center build programs reaching upwards of $500M. This growth shows no sign of stopping due to the increased demand for computing resources.

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  1. Slide 1: UPTIME INSTITUTE SYMPOSIUM CONFIDENTIAL Revolutionizing Data Center Efficiency McKinsey & Company Document Date This report is solely for the use of client personnel. No part of it may be circulated, quoted, or reproduced for distribution outside the client organization without prior written approval from McKinsey & Company. This material was used by McKinsey & Company during an oral presentation; it is not a complete record of the discussion.
  2. Slide 2: ACKNOWLEDGEMENT Unit of measure McKinsey & Company would like to thank and recognize the important collaborative contributions of Kenneth Brill and The Uptime Institute to the development of this report and its recommendations. The Institute provided critical insight based on their many years of experience as well as proprietary data and analysis not previously made public * Footnote Source: McKinsey & Company Copyright Source 2
  3. Slide 3: EXECUTIVE SUMMARY Unit of measure • The rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises: – Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continue – For many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020 • The primary drivers of poor efficiency are: – Poor demand and capacity planning within and across functions (business, IT, facilities) – Significant failings in asset management (6% average server utilization, 56% facility utilization) – Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiency • Improving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers: – Rapidly mature and integrate asset management capabilities to reach the same par as the Security function – Mandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demand – Formally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012 • To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency * Footnote Source: Source 3
  4. Slide 4: 428 DATA CENTER COST IS APPROXIMATELY A QUARTER OF TODAY’S IT COSTS . . . Unit of measure Breakdown of average IT cash costs at a typical company, percent Development 20 Application Development 40 • Not all facilities within IT budget Maintenance • Unrealistically long 20 depreciation timeframes artificially hide data center costs IT • Current constr- End Users uction boom is not a 100 15 one time catch-up investment. Server growth will require Network (LAN/ add’l new data WAN) center construction 15 every 3-5 years Infrastructure and Operations Facilities 60 8 Data Center 25 Hardware, Storage 17 Other 5 Note: Footnote * Total IT budget is illustrative of a typical company Source: Source: Source McKinsey analysis 4
  5. Slide 5: 428 AND DATA CENTER IT COSTS WILL CONTINUE TO GROW AS THE NUMBER OF SERVERS HOUSED WITHIN DATA CENTERS GROWS RAPIDLY . . . Unit of measure Servers hosted within data centers within USA • CAGR reducing due to increased use of virtualization • Power consumption per server increasing even faster as newer machines consume much more power • Data center spend 18,000 is growing rapidly due to increased Installed volume servers – U 16,000 demand 14,000 • China and other 13.6% CAGR 9.9% CAGR developing 12,000 countries are projected to grow 10,000 even more rapidly 8,000 • Growing data center spend is putting 6,000 pressure on other IT initiatives or 4,000 functions (e.g., 2,000 applications development, end 0 user computing) 2000 2001 2002 2003 2004 2005 2006 2010 Year Note: Footnote * Total IT budget is illustrative of a typical company Source: Source: Source EPA 2007 Report to congress 5
  6. Slide 6: 5,000 SERVERS AREN’T “CHEAP” BECAUSE THEY INCUR SUBSTANTIAL FACILITY (POWER AND COOLING) COSTS OVER THEIR LIFE Unit of measure Annual OpEx to support a mid-tier ($2,500) server, dollars 5/27/08 • True costs are often 4-5x the 5/27/08 cost of the server alone over a 5-10 year lifetime of a server 5/27/08 5/27/08 5/27/08 • IT hardware energy consumption drives Facility costs Facilities 5/27/08 Depreciation • Servers are often housed in 5/27/08 a higher Tier Data Center 5/27/08 than necessary, further 5/27/08 5/27/08 driving Facility costs 5/27/08 • Facility costs are growing Facility 5/27/08 more rapidly (20%) than 5/27/08 Operations overall IT spend (6%) Data center Tier II Tier III Tier IV tier * Footnote Source: Uptime Institute Source: Source 6
  7. Slide 7: 46 HIGHER LOAD DENSITY ALSO CONTRIBUTES TO HIGHER ENERGY COSTS CURRENTLY INCREASING AT 16% PER YEAR Unit of measure Total data centers energy bill, $ Billions 11.5 10.6 3 Drivers of 16% CAGR 9.3 Energy Cost Increase 8.6 7.9 • Installed base on server is 7.2 growing by 16% and 6.5 projected to grow to 41-43 million servers worldwide by 2010 • Energy consumption per server is growing by 9% as growth in performance pushes demand for energy • Energy unit price has increased an average of 4% 5/27/08 5/27/08 5/27/08 5/27/08 5/27/08 5/27/08E 5/27/08 E E Note: Weighted average consumption for top selling volume servers * Footnote Source: IDC, “Estimating total power consumption by servers in the US and the world” from Jonathan G. Koomey, Ph.D. Source: Source 7
  8. Slide 8: 22 WITHOUT RADICAL CHANGES IN OPERATIONS, MANY COMPANIES WITH LARGE DATA CENTERS FACE REDUCED PROFITABILITY Unit of measure DISGUISED CLIENT EXAMPLE Opex projection Capex projection 300 120 250 100 200 80 Rapid growth in Opex due to: Rapid growth in Capex due to: • 40% transaction volume growth • Urgent need to meet medium term • 16% database record volume growth additional demand (available capacity 150 • Trading to continue increasing at CAGR 60 projected to be fully consumed in next of 15% 30 months) • A number of business units plan to offer • Need to meet regulatory disaster new products 40 recovery goals 100 • High regional demand in Asia • Smaller data centers are out of space and • Large increase in capital spend to increase have obsolete technology depreciation expense • Inflexible configuration of the main data 50 • Additional labor to manage growing demand 20 center does not allow expansion despite • Increased facilities costs (e.g., energy) low floor density 0 0 2007 2008 2009 2010 2011 2007 2008 2009 2010 2011 • Data center cost as percent of total revenue all time high • Data center cost growing twice as rapidly as revenue • Data center construction investment significantly affects profitability for next two years * Footnote Source:Source: Source McKinsey analysis 8
  9. Slide 9: 178 DUE TO ENORMOUS ENERGY CONSUMPTION, DATA CENTERS’ CARBON FOOTPRINT IS ALSO SURPRISINGLY HIGH AND GROWING Unit of measure Key points on data centers’ Carbon dioxide emissions as greenhouse gas emissions percentage of world total – industries Percent • Data center electricity consumption is almost .5% of 0.8 1.0 world production* 0.6 • Average data center consumes 0.3 energy equivalent to 25,000 households Data Airlines Shipyards Steel • Worldwide energy consumption centers plants of DC doubled between 2000 and 2006 Carbon emissions – countries • Incremental US demand for data Mt CO2 p.a. center energy between now and 2010 is equivalent of 10 new power plants 170 146 178 142 • 90% of companies running large data centers need to build more power and cooling in the next 30 months Data Argentina Nether- Malaysia centers lands * * Including custom-designed servers (e.g., Google, Yahoo) Footnote Source: Source: Source Financial Times; Gartner report 2007; Stanford University; AMD; Uptime Institute; McKinsey analysis 9
  10. Slide 10: 86.0 ONGOING INITIATIVES NOT WITHSTANDING, EMISSIONS WILL QUADRUPLE BY 2020 CAUSING INTENSE SCRUTINY FROM Unit of measure REGULATORS, ACTIVISTS AND CORPORATE BOARDS Current technology focused initiatives Emissions are set to quadruple by 2020 will not be sufficient to reverse trend • Due to higher performance per m2, the EPA driven initiative to • The carbon footprint has electricity consumption will grow faster reduce power begun to attract scrutiny and legislation (e.g., US Public than the number of servers consumption at homes, Law 109-431 requires EPA commercial buildings, to submit a report on energy • Emission from data centers will surpass and electronics consumption of data centers those from many industry such as to US congress) Airlines • EPA has advocated use of Global consortium separate energy meters for to reduce energy large data centers and consumptions of development of procurement Emissions from Data Centers data centers standards worldwide • The European Union is Mt CO2 670 developing a voluntary Code Third party hosting service of Conduct for data centers provider based at proscribing energy efficiency Cheyenne, WY best practices. 5/27/08 powered 100% by • Data center carbon footprint wind power is expected to affect even the industries that are traditionally considered Renewable Fuels “clean” (e.g., telecom, 170 Association is a trade media, technology) group of US ethanol industry that promotes policies, research, and 5/27/08 5/27/08 regular to increase use of ethanol as fuel * Footnote Source:Source: Source IDC U.S. and Worldwide Server Installed Base 2007-11 Forecast; McKinsey analysis 10
  11. Slide 11: EXECUTIVE SUMMARY Unit of measure • The rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises: – Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continue – For many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020 • The primary drivers of poor efficiency are: – Poor demand and capacity planning within and across functions (business, IT, facilities) – Significant failings in asset management (6% average server utilization, 56% facility utilization) – Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiency • Improving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers: – Rapidly mature and integrate asset management capabilities to reach the same par as the Security function – Mandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demand – Formally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012 • To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency * Footnote Source: Source 11
  12. Slide 12: DESPITE RAPIDLY GROWING COSTS, DATA CENTERS ARE OPERATIONALLY VERY INEFFICIENT AND UNDERUTILIZED Unit of measure DISGUISED CLIENT EXAMPLE UPS, cooling, and other facilities are Server utilization remains very low. . . consistently underutilized . . . 100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 • About one third of all sites are less Up to 30% servers 30 than 50% utilized, average is 55% 20 are dead • Little co-relation between size and 20 capacity utilization 10 10 0 0 10 20 30 40 50 90 100 0 2,000 4,000 6,000 8,000 10,000 Average daily utilization (percent) Installed capacity, KW A small number of organizations are starting to monitor server utilization, however very few organizations monitor facilities energy efficiency or utilization * * Sample size – 45 data centers Footnote Source: Source: Source Uptime Institute 12
  13. Slide 13: THERE ARE FOUR PRINCIPAL CONTRIBUTORS TO DATA CENTER INEFFICIENCY ACROSS DEMAND SUPPLY FRAMEWORK Unit of measure Data center demand/supply framework 2 Demand Supply Keep as is • Poor IT capacity 1• Poor application management (e.g., low design and server utilization, low Optimization (e.g., floor utilization, limited or planning (e.g., higher rack no use of stacking, unnecessary, Business drivers poorly designed, utilization) server virtualization, (planned growth) third party hosting incorrectly configured Existing services for less critical Retain applications) facility apps, , modular design) Repurposing (e.g., up/downgrade) Business drivers (unplanned De-commission growth) Data Center Non- Expansion strategy traditional options capacity Cloud computing (e.g., Amazon S3) Application drivers 4 • Poor design and Configuration (e.g., technology (e.g., limited 1 size) use of energy efficient • Poor power & Data center Potential equipment, natural cooling design Strategy cooling, green facility leads to massive new facility Location design, siting for green waste (e.g. poor energy sources) floor lay out, un- Facility drivers Tiering utilizable capacity, and inconsistent Density Tier concepts) Financial Sourcing constraints/ imperatives Disaster Recovery 3 • Lack of critical senior executive oversight during the approval process of new data center or major upgrades (e.g., lack of validation of key assumptions and economic analysis of alternatives) * Footnote Source:Source: Source McKinsey analysis 13
  14. Slide 14: 46 1. DECISIONS ABOUT APPLICATIONS AND INFRASTRUCTURE DO NOT ADEQUATELY CONSIDER THEIR IMPACT ON DC OPERATIONS AND COST Unit of measure True Application TCO True Infrastructure TCO Not considered in TCO business Percent Percent case for ‘go/no-go’ decision ILLUSTRATIVE Application Hardware development – cost (Opex) labor/licenses Software (Opex) Maintenance and support Maintenance • Limited understanding of data center TCO (labor and parts) Servers, network, and limited access to and other hardware Network and relevant data connectivity • Limited understanding of choices that can influence data center Data center Data center cost utilization utilization • No representation of (facilities, DR) data center in design, planning, and Total cost of approval process for Total cost of application new applications and infrastructure hardware components Examples of poor application Examples of poor infrastructure decisions… decisions… • Applications that don’t reduce usage of • Storage usage not maximized monitors during off peak/closed hours • Limited use of MAID (massive array of • Limited use of grid computing idle disks) • Computation load is not shifted among • Poor layout design systems to maximize energy used • Equipment that is physically large * Footnote Source: Source: Source Uptime Institute; EPA report; McKinsey analysis 14
  15. Slide 15: 25 2. MANAGEMENT SOPHISTICATION HAS NOT KEPT UP WITH TRANSITION Utilized FROM MAINFRAMES TO DISTRIBUTED SYSTEMS Unit of measure Wasted From To Demand Demand 80% 20% In 1975-1985, mainframes with 70-80% Today, 80% of computing demand utilization handled 80% of is handled by distributed systems computing demand with 5-30% utilization * Footnote Source:Source: Source IBM Energy Efficient Data Center Jun 2007; McKinsey analysis 15
  16. Slide 16: 3. LACK OF CIO/BOARD OVERSIGHT DURING TYPICAL CAPEX APPROVAL PROCESS FOR DATA CENTERS OFTEN RESULTS IN A Unit of measure SIGNIFICANT OVERSPEND Typical CapEx approval process for data centers Requirements Design Review and approval Implementation • No active • Gold plating to • IT utilization data • Items often missed in decommissioning to “future proof” data and demand design phase (e.g., free up existing center capacity projections are migration costs create facility capacity • Limited use of seldom challenged project overruns) • Assumes highest future modular • Unitary IT solutions • Specialized project case demand expansion capacity as “fact accompli” management and projections • Lack of assumptions and cross functional • Poor demand understanding or trade offs are oversight skills often forecasting priority of IT and difficult to validate are lacking resulting • Alternate source of facility design • CXOs and boards in delays and cost supply (e.g., third choices that can often are not suffic- over runs party hosting facility) significantly lower iently knowledge- not considered power able to challenge requirements assumptions or require alternative economic choices * Footnote Source:Source: Source McKinsey analysis 16
  17. Slide 17: 4. MOST DATA CENTER FACILITIES DO NOT FULLY USE ENERGY EFFICIENT DESIGN Unit of measure SAMPLE CHALLENGES OBSERVED Temperatures in the cold aisle are much colder than required and can be increased to 74°. Similarly, the hot aisle should be hot (90° or even higher) High density air cooling usually increases total facility CapEx for electrical and mechanical capacity as well as total energy consumption. Water cooling saves energy and is simpler and more reliable All UPS modules, chillers, cooling units, etc. are installed initially instead of waiting until the center is more fully occupied Efficiency focus is on 80% or higher loads instead of the 10-30% loads where most facilities operate for much of their lives Winter free-cooling opportunities worth hundreds of thousands of dollars annually are not used because office building piping designs were used erroneously. * Footnote Source: Uptime Source Institute 17
  18. Slide 18: EXECUTIVE SUMMARY Unit of measure • The rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises: – Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continue – For many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020 • The primary drivers of poor efficiency are: – Poor demand and capacity planning within and across functions (business, IT, facilities) – Significant failings in asset management (6% average server utilization, 56% facility utilization) – Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiency • Improving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers: – Rapidly mature and integrate asset management capabilities to reach the same par as the Security function – Mandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demand – Formally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012 • To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency * Footnote Source: Source 18
  19. Slide 19: WE PROPOSE A THREE PART SOLUTION TO IMPROVING DATA CENTER EFFICIENCY Unit of measure 1 2 • Improve IT asset • Use true total cost of management capabilities ownership (TCO) of a • Improve IT demand data center by forecasting capabilities incorporating • Promote regular dialog facilities cost between business, IT, and Develop • Compute TCO over Facilities Develop entire life span of ability to data center • Use new technology to mature IT asset manage • Increase transparency increase server utilization management true cost of of data center costs • Optimize current facilities IT • Include data center utilization with a view on ownership TCO in application and power cost infrastructure decisions • Ensure that solutions are not over-designed • Include energy efficiency as an important criteria in hardware procurement Establish an integrated • Implement facilities best plan including energy practices efficiency 3 • Develop an integrated plan, measurable goals and timeline to double data center efficiency • Move accountability for facilities expense (CapEx and OpEx) and facility operations to the CIO • Appoint internal “Energy Czars” with a mandate to improve data center efficiency while maintaining business availability and reliability needs • Implement chargeback for existing apps • Improve large CapEx approval process for data centers • Publicly commit to green house gas reduction targets * Footnote Source:Source: Source McKinsey analysis 19
  20. Slide 20: Improved asset management DEVELOP MATURE ASSET MANAGEMENT AND IT PRODUCTIVITY CAPABILITIES Unit of measure Demand • Ensure technical input from solution architect during RFP/RFI process management • Aggregate pipeline forecasts with solution architect and data center operations • Use stage gate approach to qualify likelihood of demand Configuration/ • Build larger shells (or campuses of shells) by dividing floor space into smaller logical units location (“fields”) that are engineered to specific workloads and built without major M&E interruptions • Optimize current location portfolio with a view of operational and energy spend Layout/ Cabinet • Rationalize cabinet allocation by eliminate/combine cabinets with few assets and allocations discouraging allocations of space by whole cabinet to business units/ LOBs • Verify that allocated cabinets are used, don’t report allocate cabinet as used automatically • Utilize ITIL configuration mgt to track asset utilization/chargeback/de-commissioning Density • Reduce role of support infrastructure (routers/SANS) to contain density requirements • Optimize rack utilization by eliminating unnecessary peripherals and fully loading each rack Utilization • Virtualize/stack to reduce the number of physical servers; increase rack utilization • Kill comatose servers and storage as up to 30% of server may be “dead” • Enable hardware power save features • Eliminate network port redundancy Sourcing • Maintain internal control on most critical systems and co-locate less critical services • Move non critical system to managed provider in a virtualized environment with expectation to move more as the services mature and establish better track record for reliability • Include energy efficiency as an important criteria in hardware procurement Facility operations • Measure and report energy efficiency • Optimize cooling unit set-points, balance number of cooling units running, number, and location of perforated tiles with actual load • Optimize mechanical plant operation, raise chilled water supply temperature, eliminate “dueling” cooling units, utilize “free-cooling” opportunities, monitor humidification/dehumidification energy * Footnote • Seal cable openings and install blanking plates Source: Source: Source McKinsey analysis 20
  21. Slide 21: Improved asset management ENHANCE DEMAND FORECASTING CAPABILITIES Unit of measure Best practices Description Improve forecast • Track variation in forecast accuracy, incentivising business and accuracy IT to minimise deviations • Use stage gate approach to qualify likelihood of demand • Use tools and processes to capture and collate command Build dynamic • Incorporate drivers to account for organic growth, unplanned demand models business events and business cycles Value at stake from • Use scenario models to understand how different potential effective demand scenarios drive data center capacity forecasting Involve solutions • Ensure technical input from architects during design process • 15-25% reduction in architects • Ensure data center representation in projects approval process overall operational • Design Applications and hardware to optimize computing costs by avoiding overbuilds Aggressively pursue • Consider various ways to reduce data center space and power • Delayed demand reduction demands, from application and infrastructure sizing through to construction of floor optimization. incremental power • Instill culture of treating data center capacity as a scarce and and cooling capacity expensive asset rather than as a bathtub to be filled reduces CapEx Establish business- • Ensure Technology teams present clear options trading off technology dialog between key business drivers and underlying costs e.g., true cost of increments of availability, opportunity to acquire less floor space if businesses adopt wholesale virtualization, etc. Draw economic • Develop analytic approach for connecting business demand to connection between application requirements, application requirements to business demand infrastructure requirements and infrastructure requirements to and true TCO data center requirements * Footnote Source:Source: Source McKinsey analysis 21
  22. Slide 22: Improved asset management OPTIMIZE CURRENT LOCATION PORTFOLIO WITH A VIEW ON OPERATIONAL AND ENERGY SPEND Unit of measure DISGUISED CLIENT EXAMPLE Buy Prioritize for cap Hold Invest to sustain Sell No investment exit -ability building Criteria for designation Ownership Tier >10K Space/power 12-18 18-36 Location status 3/4? sq. ft? available? months months 1. US location 1, West Leased Y (3-) Y (26K) Y Buy Buy 2. UK location 1, Europe Leased Y (3) Y (16K) N Buy Buy