Pratik Chube

General Manager – Product & Marketing

Emerson Network Power

Pratik.Chube@Emerson.com

Energy LogicCalculating Data Center Efficiency –True Story of IT Energy Efficiency

Energy LogicEnergy LogicCalculating Data Center Efficiency Calculating Data Center Efficiency ––True Story of IT Energy EfficiencyTrue Story of IT Energy Efficiency

2

AgendaAgendaAgenda

� Data Center Efficiency Revisited

� Initial Steps: Reducing Data Center Energy Consumption

� A Measure for Data Center Compute Output

� Next Phase: The Four Prioritized Efficiency-Improving Actions

� Simple Tool to Measure, Prioritize and Justify Investments to Improve Data Center Efficiency .

� Click 2 Brick Virtual Data Center Build Tool.

� About Emerson.

3

Data Center Efficiency RevisitedData Center Efficiency RevisitedData Center Efficiency Revisited

Data CenterEfficiency =

Data Center Output

Energy Consumed

Two Ways to Improve Efficiency:1. Increase Data Center Output2. Decrease Amount of Energy Consumed

4

Data Center Output: No Universal Measure Exists Data Center Output: Data Center Output: No Universal Measure Exists No Universal Measure Exists

First we will address the issue of reducing energy consumption using Energy Logic, then we will turn our attention to addressing data center output.

Simple Data Center Layout(Energy Demand, Distribution and Supply)Simple Data Center LayoutSimple Data Center Layout(Energy Demand, Distribution and Supply)(Energy Demand, Distribution and Supply)

Energy Logic Model5,000 square foot Data CenterEnergy Logic ModelEnergy Logic Model5,000 square foot Data Center5,000 square foot Data Center

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Energy Logic: The ‘Cascade’ EffectEnergy Logic: The Energy Logic: The ‘‘CascadeCascade’’ EffectEffect

1 Watt saved at the server component levelresults in cumulative savings of about

2.84 Watts in total consumption

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Energy Logic:Prioritized Energy Saving StrategiesEnergy Logic:Energy Logic:Prioritized Energy Saving StrategiesPrioritized Energy Saving Strategies

© 2007 Emerson Network Power

Higher AC voltage improves efficiency

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Energy Logic Addresses Space, Power & Cooling ConstraintsEnergy Logic Addresses Space, Power Energy Logic Addresses Space, Power & Cooling Constraints& Cooling Constraints

© 2007 Emerson Network Power

65% Space Freed Up43% Cooling Capacity and33% Power Capacity Saved

BEFORE

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5,000 sq. ft. / 465 sq. m.

1,768 sq. ft. / 164 sq. m

AFTER

10

Energy Logic:Payback PeriodEnergy Logic:Energy Logic:Payback PeriodPayback Period

© 2007 Emerson Network Power

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Energy Logic: 4 Key TakeawaysEnergy Logic: 4 Key TakeawaysEnergy Logic: 4 Key Takeaways

1. Start by reducing consumption at the IT equipment level and then work your way back through the supporting equipment

Every Watt saved at the equipment level has a cascading effect upstream.

2. Availability & Flexibility do not have to be compromised in order to increase data center efficiency

- Efficiency Without CompromiseTM

3. High Density Architecture contributes toward increased efficiency- IT Consolidation, Cooling Efficiencies

4. In addition to improving energy efficiency by reducing consumption, implementing these strategies frees up capacity of key constraints: Power, Cooling & Space

Energy Logic White Paper Availablehttp://www.liebert.com/common/ViewDocument.aspx?id=880

© 2007 Emerson Network Power

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Data Center Efficiency:Importance of Measuring Data Center OutputData Center Efficiency:Data Center Efficiency:Importance of Measuring Data Center OutputImportance of Measuring Data Center Output

� A Measure of Data Center Output is needed to help drive the right behavior for improving efficiency

� Lack of output metric limits focus and attention

– to the infrastructure (supply) side rather than on both the IT (demand) and infrastructure sides

– to consumption rather than on both output and consumption

Data CenterEfficiency

=

Data Center Output

Energy Consumed

13

Measuring Data Center Output:ChallengesMeasuring Data Center Output:Measuring Data Center Output:ChallengesChallenges

� Data centers perform different types of work

– Processing-intensive for scientific and financial applications

– Data transfer-intensive for Web-based applications

� Data center requirements change as mix of workload shifts

� However, industry experts can agree that performance has improved dramatically over the last 5 to 10 years

14

0%

1000%

2000%

3000%

4000%

5000%

6000%

7000%

8000%

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

IT Performance Improvement: 2002 – 2007IT Performance Improvement: 2002 IT Performance Improvement: 2002 –– 20072007

Source: Electronics Cooling magazine (Feb 2007)Belady, C., P.E., Hewlett-Packard, ‘In the Datacenter, Power & Cooling Costs More than IT Equipment it supports”

75X

10X

1998 – 2007 : 7400% Improvement (75x)

2002 – 2007 : 650% Improvement (7.5x)

Ra

w P

erf

orm

an

ce

Ga

in

1X

15

IT Performance Improvement: 2002 – 2007IT Performance Improvement: 2002 IT Performance Improvement: 2002 –– 20072007

Source: Intel

Intel x86 2002 2007

TFLOPS 3.7 3.7

Servers 512 53 blades

GFLOPS/server 7.2 69.8

7.2 GFlops/Server

69.8 GFlops/Server 2002 – 2007

870% Improvement

(9.7x)

2002

2007

16

Introducing “CUPS”Introducing Introducing ““CUPSCUPS””� We introduce CUPS, or Compute Units per Second,

as a temporary or placeholder measure for what will be the eventual universal metric for data center output

© 2007 Emerson Network Power

Data Center

Efficiency = =

CUPS

WattsConsumed

Data Center Output

Energy Consumed

Based on information on performance gains, we assume CUPS has improved by

7x between 2002 and 2007

(compared to 7.5x Belady; 9.7x Intel)

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How Does CUPS fit with Moore’s Law?How Does CUPS fit with MooreHow Does CUPS fit with Moore’’s Law?s Law?

CUPS

5899

9351

0

2000

4000

6000

8000

10000

2002 2007

Total Data Center Power Draw (MW)

Server and Data Center Output andEfficiency Improvement 2002 - 2007Server and Data Center Output andServer and Data Center Output andEfficiency Improvement 2002 Efficiency Improvement 2002 -- 20072007

2293

4027

0

1000

2000

3000

4000

5000

2002 2007

Total Server Power Draw (MW)

1.8X

1

7

0.0

2.0

4.0

6.0

8.0

2002 2007

Server Performance (MCUPS / Server)

7.0X 14.0X

321

2432

0

500

1000

1500

2000

2500

3000

2002 2007

Server Efficiency (CUPS / Server Watt)

1048

125

0

200

400

600

800

1000

1200

2002 2007

Data Center Efficiency (CUPS / Datacenter Watt)

8.4X7.6X

0.7

9.8

0.0

2.0

4.0

6.0

8.0

10.0

12.0

2002 2007

Total Compute Output (TCUPS)

18

1.6X

19

Data Center EfficiencyImproved Dramatically from 2002 to 2007Data Center EfficiencyData Center EfficiencyImproved Dramatically from 2002 to 2007Improved Dramatically from 2002 to 2007

� Server efficiency improved over 650% (7.6x)

� Data Center efficiency improved over 735% (8.4x)

If computing demand in 2007 was the same as in 2002, 2007 power consumption would have been <1/8th of 2002 consumption.

59%

1300%

738%

0%

200%

400%

600%

800%

1000%

1200%

1400%

% I

ncre

ase

2002 - 2007

Consumption

Output

Efficiency

Gets the Most Attention

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Efficiency Improvement: Cars vs. ComputersEfficiency Improvement: Efficiency Improvement: Cars vs. ComputersCars vs. Computers

If fuel efficiency had kept pace with data center efficiency improvement, cars would get

163 miles to the gallon!

CAGR53.0%

CAGR0.8%

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IT Efficiency in PerspectiveDramatic Impact on Business, Economy, SocietyIT Efficiency in PerspectiveIT Efficiency in PerspectiveDramatic Impact on Business, Economy, SocietyDramatic Impact on Business, Economy, Society

� Significant improvement in productivity through automation of tasks and processes

� Better and faster decision making driven by availability of richer real-time information and communication

� Wider utilization of best cost resources around the world, driving global economic development

� Increased level of conveniences and benefits at the individual and societal level

� 10X productivity gain across all industries per KW ICT*

Increase in energy consumption has been small relative to increase in output -- and

benefits to economy and society.* ACEEE: Information and Communication Technologies: The Power of Productivity, Report # E081

Applying Energy Logic:Improvements in Compute EfficiencyApplying Energy Logic:Applying Energy Logic:Improvements in Compute EfficiencyImprovements in Compute Efficiency

0 500 1000 1500 2000

Base

High Efficiency Power Supply

Power Management Features

Blade Servers

Virtualization

Power Distribution Architecture

Cooling Best Practices

Variable Capacity Cooling

High Density Cooling

Monitoring & Optimization

604 1,335

5 IT Actions

2,198

CUPS / Datacenter Watt ����

Server Replacement

2.2x Efficiency Improvement!

All Ten Energy Logic Steps

3.6x Efficiency Improvement!!

5 InfrastructureActions

22

Low Power Processor

1,673

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Energy Logic: Measuring Data Center Efficiency - 4 Key StepsEnergy Logic: Measuring Data Center Energy Logic: Measuring Data Center Efficiency Efficiency -- 4 Key Steps4 Key Steps

� Most impactful ways to improve data center efficiency:

1. Speed up refresh cycle for IT technology

• Blades provide a modular platform for continued improvement

2. Implement server power management policies

3. Virtualize

4. Adopt a high-density architecture

Energy Logic: Measuring Data Center Efficiency

http://www.emerson.com/edc/docs/EnergyLogicMetricPaper.pdf

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Defining Criteria for aData Center Efficiency MetricDefining Criteria for aDefining Criteria for aData Center Efficiency MetricData Center Efficiency Metric

� A Measure of Data Center Output, even if less-than-ideal, can help drive the right energy-saving behaviors

– Effective measure vs. Ideal or Fair Measure

Data Center

Efficiency=

Data Center Output

Energy Consumed

� Three criteria an effective measure must fulfill:

1. Most importantly, does it drive the right behavior?

2. Must be published at device level so that users can evaluate competing technologies

3. Must be scalable to the data center, allowing the output of the devices to be added together to produce an overall measure of data

center efficiency

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Energy Logic ShowsUsing PUE Does Not Drive Right BehaviorEnergy Logic ShowsEnergy Logic ShowsUsing PUE Does Not Drive Right BehaviorUsing PUE Does Not Drive Right Behavior

1

2

3

Total Facility

Power

(Mega Watts)

IT Equipment

Power

(Mega Watts)

PUE

Un-optimized Data Center

1.127 0.588 1.9

Five IT Actions Only

0.713 (-37%)

0.370(-37%)

1.9

Five Infrastructure Actions Only

0.858 MW(-24%)

0.582(-1%)

1.5

*PUE: Power Usage Effectiveness

PUE =Total Facility Power

IT Equipment Power

Using PUE does not drive the right behavior.

PUE does not change even though energy consumption reduces by 37%!!

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Computing Output

(Mega CUPS)

Total Facility Power

(Mega Watts)

Data Center Efficiency

(CUPS / Watt)

Un-optimized Data Center

680.96 1.127 604

Five IT Actions Only1,192.31

(75%)0.713(-37%)

1,673(+177%)

Five Infrastructure Actions Only

680.96(0%)

0.858(-24%)

794(+31%)

Fully Optimized Data Center

1,192.31(75%)

0.5425(-52%)

2,198(+264%)

Energy Logic ShowsCUPS / Watt Drives Right BehaviorEnergy Logic ShowsEnergy Logic ShowsCUPS / Watt Drives Right BehaviorCUPS / Watt Drives Right Behavior

1

2

3

4

Data Center

Efficiency=

Data Center Output

Energy Consumed

CUPS / Watt drives the right behavior.

27

Simple Tool for Assessing Data Center EfficiencySimple Tool for Assessing Data Center EfficiencySimple Tool for Assessing Data Center Efficiency

� IT and Data Center Managers need a way to:

– Compare and prioritize data centers for:

• Efficiency improvement actions

• Space / Power / Cooling constraint relieving opportunities

– Justify IT investments in new technologies to management

– Track data center efficiencies over time

Energy Logic provides a Sample Template that can be used to accomplish these tasks

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Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Complete this Sample Template for each Data Center Location

Columbus Data Center Jan 1, 2009

This sample template is a Simple Tool that only accounts

for servers. It can easily be modified to account for other

IT equipment as data becomes available.

Data Center Efficiency Tool available on-line at: http://www.emerson.com/edc/Calculator/default.aspx

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Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

In the absence of an industry standard for computing output, Energy Logic II provides MCUPS estimates to use as a starting point.

Columbus Data Center Jan 1, 2009

Estimated Output per Server can be

modified based on your specific situation.

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Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 1For a given location, fill in the number of servers / blade servers purchased each year.

50

25

25

Columbus Data Center Jan 1, 2009

31

Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 2Enter the average server utilization rate for each ‘year’ of servers.

16%

65%

20%

Columbus Data Center Jan 1, 2009

Note: Higher Utilization due to virtualization.

32

Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 3For each row, multiply Columns A, B, and C to calculate total computing output of servers from each year of purchase.

= 8 MCUPS50 16%X X

Columbus Data Center Jan 1, 2009

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Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 4Add up the Total Output of each row and enter the total into the Total Data Center Output field.

+

50

25

25

16%

65%

20%

8

52.8

35

95.8

Columbus Data Center Jan 1, 2009

34

Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 5Enter the Total Energy Consumption (Mega Watts) for the data center into Field E.

0.25

35

Data Center Efficiency – Sample TemplateData Center Efficiency Data Center Efficiency –– Sample TemplateSample Template

Step 6Calculate Data Center Efficiency by dividing Total Data Center Output by Total Energy Consumption.

383.2

0.25

95.8

For the first time, we have an understanding of the true data center efficiency.

36

Data Center EfficiencyUsing Sample Template to Compare LocationsData Center EfficiencyData Center EfficiencyUsing Sample Template to Compare LocationsUsing Sample Template to Compare Locations

Select data center locations on which to focus efforts using Data Center Efficiency and Total Energy Consumption measures.

383.2

0.25

95.8

Now that we know the efficiency of each of our data centers, we can compare each of our locations.

537.5

0.2

107.5

A

B

37

Data Center EfficiencyUsing Sample Template to Justify InvestmentData Center EfficiencyData Center EfficiencyUsing Sample Template to Justify InvestmentUsing Sample Template to Justify Investment

Columbus Data Center Jan 1, 2009

50

25

25

16%

65%

20%

8

52.8

35

X 25

25 35%

4

90

X

Once specific actions to take have been identified, ‘before’ and ‘after’ templates can be used to justify the investment.

After

38

Data Center EfficiencyUsing Sample Template to Justify Investment

Data Center EfficiencyData Center EfficiencyUsing Sample Template to Justify InvestmentUsing Sample Template to Justify Investment

The quantified improvement in Data Center Efficiency as well as the lower energy costs from the reduction in Total Energy Consumption provide meaningful data to

management to justify project investment.

383.2

0.25

95.8

To estimate energy consumption in the ‘after’ scenario, calculate energy savings of new servers and apply estimated cascade

effect multiplier. 1 (conservative) to 1.8 is recommended

909.0

0.2

181.8

Be

fore

Aft

er

39

Data Center EfficiencyTrack Each Location Over TimeData Center EfficiencyData Center EfficiencyTrack Each Location Over TimeTrack Each Location Over Time

Track performance over time to identify trends, bring attention to efficiency

improvement efforts, and to establish a process of continuous improvement.

383.2

0.25

95.8

909.0

0.2

181.8

2008

2009

1145.0

0.3

343.5

2010

40

IT & Data Center Managers Are AskingIT & Data Center Managers Are AskingIT & Data Center Managers Are Asking

� How to compare efficiencies across data centers,

to prioritize for action

� What specific actions to take to improve

efficiency

� How to justify IT investments to management

� How to track efficiencies over time

Data Center Efficiency Tool available on-line at: http://www.emerson.com/edc/Calculator/default.aspx

White Paper available at: http://www.emerson.com/edc/docs/EnergyLogicMetricPaper.pdf

Data Center – Web ConfiguratorData Center Data Center –– Web ConfiguratorWeb Configurator

Design-IT-Your Self Data Center Configuration Tools

www.EmersonNetworkPower.co.in/tools

About Emerson

• Founded in 1890 in St. Louis, MO

• 136,000 Employees worldwide, 245 Mfg Locations, 150 Country Operations

• Outstanding Management Practices & Solid Financial Position

• FY 07 Sales Revenue - $24.8 billion, EPS – 3.11 $, Market Cap – $ 50 billion

• Top 100 companies for IT Excellence – CIO Magazine

• 100% of Fortune 500 company rely on Emerson to protect their critical IT infra.

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Thank You !Thank You !Thank You !Pratik Chube

pratik.chube@emerson.com