Revolutionizing Data Center Efficiency Presented by Kenneth G

Revolutionizing Data Center Efficiency Presented by Kenneth G. Brill, Executive Director, Uptime Institute

Agenda • Background • Revolutionizing Data Center Efficiency • Findings • Primary drivers of poor efficiency • Recommendations • 2, 012 goal

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Mc. Kinsey Report: Revolutionizing Data Center Efficiency • Uptime Institute gratefully acknowledges Will Forest and Mc. Kinsey and Company for their contributions to data center efficiency • First presented at the Institute’s 08 Symposium, the original Report is available for free download at www. uptimeinstitute. org/resources • This presentation retains all original findings, poor efficiency drivers, recommendations and the implementation goal • The supporting material is sometimes different • Corporation Average Datacenter Efficiency (CADE) is better defined with a new four year example

Mc. Kinsey Report Finding #1 • “Data center facilities spend (Cap. Ex and Op. Ex) is large and quickly growing…in many technology intensive industries…” • “Some intensive data center users will face meaningfully reduced profitability if current trends continue”

For IT-Intensive Businesses, Fundamental IT Economics Have Changed • Facilities Op. Ex is 8% of IT’s budget and is growing at 20% annually • Finding these costs may require forensics as significant facility costs (especially depreciation) may not be within IT • If IT’s annual budget growth remains at 6%, Facilities cost growth will drive out new application development • If IT’s budget growth is increased, corporate profitability will be meaningfully reduced

Data Center Accounts For A Quarter Of IT’s Annual Budget Application Development 20 Development 40 Maintenance 20 Facilities IT Budget 8% Data Center 100% 25% Infrastructure & Operations 60% End Users Network (LAN/WAN) 15 Hardware, Storage, Ops 15 Other 5 Source: Mc. Kinsey; breakdown shown is typical of an IT intensive business 17%

IT Has Become A Major Portion Of Corporate Fixed Assets • IT capital assets now represent 50% or more of total fixed assets in other than heavy manufacturing or basic industries • And data center facilities are now becoming 50% of the 50% Source: Harvard Business Review article

1, 000 Additional Physical Servers Per Year Results In Construction Boom 13. 6% CAGR 9. 9%CAGR 9. 9% Sources: EPA 2007 Report to Congress, Vernon Turner/IDC, Koomey 2007

7. 5% CAGR 27% 100 6. 3% CAGR 12% DATA CENTER ENERGY CONSUMPTION BILLION KWH 120 15. 7% CAGR 13. 8% CAGR 80 10 Power Plants Data Center Energy Consumption Is Growing! 60 40 2001 2002 2003 2004 2005 2006 2007 2010 YEAR Source: Uptime Institute Network members: Top Quartile sites – avg 32 K SF Source: Uptime Institute Network members: Average sites – avg 33 SF Sources: EPA 2007 Report to Congress, Vernon Turner/IDC, Koomey 2007

Servers Aren’t As Cheap As They First Appear • Tier IV data center space, power and cooling costs per server* • Cap Ex investment per server $15, 400 • Charge back • Electricity 2, 020/yr 470/yr $8, 080/4 years 1, 880/4 years * IT acquisition cost per server of $1, 500 to $2, 500

Site Costs Per $2, 500 Server (Assumes Low Cost location And Institute Best Practices) Site Concurrent Maint. /Fault Tolerance Site Costs (USA) Tier III Tier IV Cap. Ex per $8, 300 % $14, 000 % $15, 400 % Server* Annual Expense Site Depr’n 550 42 Electricity 420 32 Site Operations 350 26 Total per Server $1, 320 100 * 55% site asset utilization 950 50 1, 000 44 420 23 500 27 $1, 870 100 470 33 550 23 $2, 020 10 0

Embedded IT Watts Per $1, 000 Of Server Spending

(Logarithmic scale) Root Cause: Power Efficiency Lagging Moore’s Law Results In Rising Site TCO

Summary Of Finding #1 “Meaningfully Reduced Profitability” • Increasing facility costs (forensics may be required) have unfavorably changed IT’s fundamental economics • • Choke out new application development or Meaningful reduction in corporate profitability • Servers aren’t necessarily cheap • An unintended consequence of Moore’s Law success has now become a “disruptive technology” • Root cause of the economic change has been “invisible” to most senior executives

Mc. Kinsey Report Finding #2 • “For many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions”

Data Centers Are Major Energy Consumers Attracting Government Attention • Public Law 109 -431 mandated study to determine US data center energy consumption • EPA Report to Congress, August 2007 • Data centers consumed 1% of US electricity production in 2000 • • • 2% in 2005 • Fastest growing industrial segment in the economy Projected 3% in 2010 Comparable to energy consumption of all televisions, but growing much more rapidly

Data Centers Are THE Most Energy Intensive Asset In Most Companies • Just three data centers out of 3, 400 street addresses account for 10% of total energy consumption for a major financial • Data center energy intensity is 20 to 100 times that of an office building • A single data center can consume the energy equivalent of 25, 000 homes

Summary Of Finding #2 Greenhouse Gasses • The energy required to power and cool a single “cheap” server emits 4 tons of GHG per year • 15 million servers in 2010 equals 60, 000 tons annually of GHG • Governments can’t help but become very concerned with data center energy efficiency

Mc. Kinsey Report: Primary Drivers Of Poor Efficiency • “Poor demand capacity planning and management within and across functions…” • “Significant failings in asset management” • “Boards, CEOs, and CFOs are not holding CIOs accountable…”

Server Assets Are Dramatically Underutilized 100 90 Source: Mc. Kinsey Disguised Client 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 Average Daily Server Utilization in Percent

Facility Capacity Utilization in Percent Installed Data Center Power And Cooling Capacity Is Underutilized 100 80 60 40 20 0 2, 000 4, 000 6, 000 8, 000 Installed Facility Capacity in k. W 10, 000

Application And Infrastructure Decisions Typically Fail To Include True Total Costs True Application TCO Application dev labor and licenses Main and support Servers, network, and other hardware DC utilization (availability, redundancy, DR, infrastructure, facilities) True Infrastructure TCO Hardware cost (Op. Ex) Software (Op. Ex) Maintenance (labor & parts) Network & connectivity DC utilization (availability, redundancy, DR, infrastructure, facilities) Not Typically Considered in Business Case TCO for Go/No Go Decisions Source: Mc. Kinsey analysis, EPA Report, Uptime Institute

Managerial And Economic Sophistication Has Not Kept Up • In 1975 -1985, mainframes with 70% to 80% utilization handled 80% of compute demand • Today, 80% of compute demand is handled by distributed systems with 5% to 30% utilization

Lack Of Data Center Cap. Ex Oversight Has Resulted In • Failure to free up existing capacity by decommissioning comatose servers • Anticipating unknown business requirements results in overbuilding • Sizing being based on highest case demand • Sweet spot costs and alternate solutions not being understood/considered • Inadequate cross functional, financial analysis and specialized project management skills leading to major items being missed resulting in delays and overruns

Summary: Primary Drivers Of Poor Efficiency • “Poor demand capacity planning and management within and across functions…” • “Significant failings in asset management” • “Boards, CEOs and CFOs are not holding CIOs accountable…”

Mc. Kinsey Report Recommendations • “Rapidly mature and integrate asset management capabilities…” • “Mandate the inclusion of true total cost of ownership … in business case justification of new products and applications to throttle excess demand” • “Formally move accountability for data center facilities and operations to the CIO and appoint internal ‘Energy Czar’…”

Mc. Kinsey Report: 2012 Goal • Double data center efficiency by 2012 as measured by CADE (Corporate Average Datacenter Efficiency

Energy Czar IT Initiatives • Kill comatose servers • • 100% kill of 15% accumulated comatose servers Implement formal de-commissioning program using ITIL to document, bill back and audit • Virtualize • • 40% of applications 5 to 1 collapse • Buy 20% more energy efficient hardware • • More efficient power supplies and better fans Rightsize memory

Energy Czar Facilities Initiatives • 12% improvement in site energy efficiency • Measure site infrastructure energy overhead (Green Grid 1/DCi. E or PUE if all forms of energy are included over a 12 month period) and correct any surprises • • • Correctly set cooling unit set points Truly implement hot/cold aisle concepts Eliminate humidification/de-humidification Turn off unneeded cooling units If available, increase waterside free-cooling

Baseline And Fourth Year Server Qty, IT And Utility k. W And GHG Baseline 2012 Year No Chg Czar One App/One Server Virtualized Apps Active Applications 6, 200 0 6, 200 10, 900 6, 500 4, 400 10, 900 Comatose Servers 1, 100 3, 000 0 IT Plug Load – k. W Utility Load – k. W 2, 200 4, 800 6, 000 11, 800 3, 100 5, 300 242* 130* 4 Year CO 2 emissions * Cumulative over four years in thousands of tons

Cumulative Four Year Outcome Financial 2008 - 2012 No Chg Czar IT server Cap. Ex expenditures $31 $20 Site asset Cap. Ex expenditures 112 0 54 33 $197 $53 4 Year IT + Site Op. Ex expenses Total Cap. Ex + Op. Ex All numbers in millions of dollars

Corporate Average Datacenter Efficiency (CADE) ver 1. 0 CADE = IT Efficiency IT Asset IT Energy Utilization x Efficiency x Site Efficiency Site Asset Site Energy Utilization x Efficiency

CADE Calculation -- Baseline Year Server Based Compute Load • IT Efficiency • IT asset utilization* (10% on active apps, 8. 5% overall due to comatose servers) • IT energy efficiency (assume 5% for base year) • Facility Efficiency • • Site asset utilization* (55%) Site energy efficiency** (46%) (use a 12 month moving average) • CADE = 8. 5% x 55% x 46% x 100 = 11 * Asset utilization = IT Plug load/site asset capacity ** Same as Green. Grid’s DCi. E except including all forms of energy running average over 12 months

CADE: Baseline And Fourth Year Baseline 2012 Year No Chg Czar IT asset utilization 9% 8% 15% IT energy efficiency 5% 5% 76% Site asset utilization 55% 77% Site energy efficiency 46% 51% 58% 11 15 500 CADE

Energy Czar Fourth Year And Cumulative Results • Mc. Kinsey Report Goal for 2012 was a doubling of data center efficiency • Continuation of current trends fails (CADE is slightly improved from 11 to 15, minimum CADE goal would have been 33) • Energy Czar program more than succeeds • CADE goes from 11 to 500 for an efficiency increase of 46 times • Financially, $144 million is saved over four years • 112, 000 tons of GHG emissions are avoided

Revolutionizing Data Center Efficiency Summary • Findings • Primary drivers of poor efficiency • Recommendations • 2012 goal

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