Green Capacity Planning: Theory and Practice · 2020-02-20 · future business growth Two year...

© 2009 Optimal Innovations, HyPerformix & LSI

ptimal

Innovations

Amy Spellmann, Optimal Innovations

Richard Gimarc, HyPerformix

Charles Gimarc, LSI

Rocky Mountain CMG

June 02, 2009

Green Capacity Planning:Theory and Practice

2

Agenda

� Green Capacity Planning�Methodology

�Energy Footprint Projection

� Case Study�Server Upgrade Scenario

�Virtualized Scenario

�Storage Upgrade Options

�Comparison

� Conclusion

3

Green Capacity Planning:What is it?

� Extends established methods & best practices

� Introduces an energy energy footprint projectionfootprint projection

� Views energy as a new resource for capacity planners to include in their analysis

� Focuses on cost-effective technologies & practices to manage today’s challenges

4

Green Capacity PlanningCombines Tools & Best Practices

Predictive Capacity Planning

Energy FootprintProjection

Cost & Capacity Comparison

+ =

Server Upgrade: Energy Footprint per Month

0

5,000

10,000

15,000

20,000

25,000

Ap

r 2

00

8

Ma

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00

8

Ju

n 2

00

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Oct

20

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20

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Oct

20

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9

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9

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20

10

Fe

b 2

01

0

Ma

r 2

01

0

Ap

r 2

01

0

kW

h p

er

Mo

nth

SU:EFP-Storage (kWh) SU:EFP-Web (kWh) SU:EFP-Image (kWh) SU:EFP-CacheApp (kWh)

SU:EFP-DB (kWh) SU:EFP-CacheFill (kWh) SU:EFP-Site (kWh)

Commercial Capacity Planning Tools

Professional Services

Green CapacityPlanning

Monthly Energy Footprint Comparison

[Energy-IT + Energy-Site]

0

5,000

10,000

15,000

20,000

25,000

Apr

200

8

Ma

y 20

08

Ju

n 2

00

8

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l 20

08

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g 2

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8

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8

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Nov 2

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n 2

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Mar

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9

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Oct 2

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9

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c 2

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9

Ja

n 2

01

0

Fe

b 2

01

0

Mar

201

0

Apr

201

0

kW

h p

er

Mo

nth

SU:Total kWh V:Total kWh

Established Methods

New Resource to Evaluate

5

Green Capacity Planning:Why do we need it?

� By 2010, IDC expects that for every dollar of new server spending, an additional $0.70 will be needed for power and cooling

� 2% of the world’s carbon dioxide emissions come from IT equipment--the same amount of pollution as the airline industry creates – Wall Street Journal

� With the advent of high density computer equipment such as blade servers, many data centers have maxed out their power and cooling capacity - Gartner

� New data center construction projects cost $250M and up

� Google spent $600M each for four new data centers in 2007

6

Green Capacity Planning:Value

� Determines the most efficient way to meet business needs while minimizing computing, power, cooling, space requirements

� Assures SLAs are met

� Response times

� Utilization

� Evaluates potential cost & energy savings of virtualization

MinimizeMinimizeCostCost

Meet Meet SLAsSLAs

Optimize Optimize HardwareHardwareUtilizationUtilization

7

Green Capacity Planning:Factors

Cost Effective SolutionCost Effective Solution

IT Energy

Site Energy

Energy Cost

Space

Performance

IT Equipment

� Must meet SLAs

� Response times

� Utilization

� Minimize power, space, cooling

� Evaluates potential cost & energy savings of virtualization

8

Methodology:Enhanced Traditional Approach

Business Requirements: Profile & SLA

DataCenterUpgrade Plan

IT Equipment Capacity & Workload Analysis

Energy Footprint Analysis

Infrastructure Capacity Analysis

Cost Calculation: IT Equipment,

Infrastructure & Energy

Infrastructure & Energy

Loop until Optimized

Traditional

Capacity Planning

EFP

9

Green Capacity Planning Metrics

� New Inputs�Energy Footprint (calculated or measured)

�PUE

�Active Idle

�Max Power

�Cost of electricity

� Comparison Metrics�Energy Footprint Projection

�Rack Space

�Cumulative Costs

�Productivity (Trans per kWh)

In addition to traditional capacity planning

metrics, e.g., Utilization,

Response Time, etc.

10

Energy Footprint Projection

� Determine energy required by each system component� Obtain active-idle, average,

maximum power from measurements or vendor specifications

� Compute energy usage over time (e.g., monthly)

� Add “site” infrastructure energy� Overhead energy for

running components in the data center

� Includes: cooling, fans, power distribution units, lighting, etc.

11

Energy Footprint Projection:Terminology

� Terminology� Power

� Spot measurement, single point in time

� Measured in watts (W)

� Energy� Power consumption over a period of time

� Measured in kilowatt hours (kWh)

� You pay for energy, not power

� Energy estimation� Component-level power requirements (W)

� Estimate consumption over a period of time (e.g., 1 day)

Power ≠ Energy

Power x Time = Energy Usage

12

Energy Footprint Projection:Data Center Energy Usage

General Flow� Energy enters in the form of electricity� IT equipment used to store &

manipulate information� IT equipment generates heat� Site infrastructure removes generated

heat

Data Center

Electricity

Site Infrastructure

IT Equipment

Questions � What if your data center’s power

input is maximized?� How can you increase your compute

power without increasing your power draw?

� Can you improve site efficiency?� Do you really need to build a new

data center?

13

Energy Footprint Projection:Components

� Two components� IT Equipment

� Site Infrastructure

� Approach� Compute IT energy

� Estimate Site energy

� Power Usage Effectiveness

� Today’s average of 2.0

� Use to estimate Site energy

� Energy = Power X Time Site - EstimateIT - Compute

EFP(Day) = IT_kWhComputed + Site_kWhEstimated

1.0PowerIT

PowerITPowerSitePUE ≥

+=

14

Energy Footprint Projection:Component Contribution

IT Equipment [EPA2007]

Site Infrastructure [ACC2008]

Fans - 16%

Transformer - 5%

Lighting - 8%

UPS - 17%

Chillers - 54%

Network - 10%

Storage - 10%

Servers - 80%

15

EFP(Day) = IT_kWhComputed + Site_kWhEstimated

Power Usage Effectiveness:

Estimate Site_kWh using assumed PUE:

Energy Footprint Projection:Estimating EFP(Day)

Given Poweri for each IT device:

EFP(Day) = PUE x IT_kWhComputed

1111PowerPowerPowerPowerITITITIT

PowerPowerPowerPowerITITITITPowerPowerPowerPowerSiteSiteSiteSitePUEPUEPUEPUE ≥+

=

Site_kWhEstimated = IT_kWhComputed x (PUE – 1)

IT_kWhComputed = (∑Poweri) x (24 Hours/Day)

16

Energy Footprint Projection: Server Power Usage

� How do you estimate the power usage of a server?

� Power specifications for a server (W):

� Power supply

� Max Power

� Active Idle

( ) IdleW100

Util%IdleWMaxW%Power@Util +

×−=

Idle

Max

0

50

100

150

200

250

300

0% 20% 40% 60% 80% 100%

UtilizationA

vera

ge P

ow

er

(W)

� Approximation

� Server power usage scales linearly with processor utilization

� Validated across various servers ± 5% [TGG2008]

17

Energy Footprint Projection:Server Power Usage - Example

( ) IdleW100

Util%IdleWMaxW%Power@Util +

×−=

221.9Power@70% =

Assume server power usage scales linearly with CPU utilization

( ) 147100

70147254Power@70% +

×−=

147

254

0

50

100

150

200

250

300

0% 20% 40% 60% 80% 100%

Actual Load

Avera

ge P

ow

er

(W)

18

Demonstrate Green Capacity Planning

� Extend data center lifetime while supporting future business growth�Two year planning based on business projections

�Limited data center power

� Develop a capacity plan for growth/change�Upgrade/add servers

�Leverage virtualization

� Align with business requirements�Predict required infrastructure to meet SLAs

�Reduce today’s energy footprint

�Select cost-effective solution

19

Case Study:eCommerce System

� Growing eCommerce system

� Business Requirements

� 5% monthly growth

� 2-year upgrade plan to support growth & meet SLAs

� Reduce IT & site energy costs

� Optimize hardware utilization

� Evaluate virtualization as an energy saving solution

Baseline System� 24 servers

� 42U Rack space

20

Case Study:Baseline System

� TPC-W Benchmark �Published TPC-W Benchmark Result

�May 2002

�Full Disclosure Report - details

�Simulates Web-based browsing & shopping

�Provides the baseline specification �Application architecture

�IT equipment

�Workload

�Utilization Benchmark Workload� 100,000 Items

� 76,000 Users

� 9,709 WIPSb@100,000

21

Case Study:Baseline Architecture & IT

22

Case Study:Baseline Database Storage

� 12 HDD per enclosure

� Ultra320 SCSI

� 5 enclosures

� 3U rack space per enclosure

� 41 total disks

8120243, 4, 5Database

393572Backup

1351 / 0101Log

Capacity(GB)

RAIDLevel

# ofDisk

EnclosurePurpose

3U

12

Log

23

Case Study:Database Technology Upgrade

� Move to current SAS technology that will allow scaling for 2 years

� Replace original Ultra 320 SCSI storage subsystem with newer SAS (Serial Attached SCSI) technology

� Reduce power by changing from 3½” to 2½” SFF disks

� Triple storage density by moving from 12 disks in a 3U space to 24 disks in a 2U space

� Achieve higher performance to meet the demands of the system after 2 years of workload growth

� Decrease power per enclosure by 50%

12 x 3U 24 x 2U

Ultra320 SCSI

3Gb/s SAS

24

Case Study:Modeling Approach

� Utilizing HyPerformix’s Capacity Manager for Capacity Analysis

� Dell’s Datacenter Capacity Planner for Power Metrics

� Integrating results for Server/Resource Capacity, Power Requirements & Business Metrics

25

Case Study:Modeling Scenario Strategy

Server Upgrade

� 5% monthly growth for 2 years

� 70% capacity threshold

� Optimize the number of upgrades

� Dell PE 2950 for the database

� Dell PE 1850 for all other servers

Virtualization





� Virtualize all other servers on a pool of Dell PE 1950s

� Optimize use of virtual hosts

For each scenario1. Determine infrastructure required to support

workload growth (Capacity Manager)2. Estimate energy footprint3. Calculate cost of IT equipment & energy

26

Database Storage Growth

0

4

8

12

16

20

24

28

32

36

40

Apr-

08

May-0

8

Jun-0

8

Jul-08

Aug-0

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Sep-0

8

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08

Nov-0

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Jan-1

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0

Mar-

10

Apr-

10

Nu

mb

er

of D

isks

Log Backup Database

Scenario - Server Upgrade:Database Storage Provisioning

� Growth Assumption:� Database storage grows at

same rate as workload (5% per month)

� Baseline Assumptions:� Use same capacity disks

� 36GB for Log & Database� 73GB for Backup

� Same disk fill factor� One RAID adapter per

enclosure

� Method:� Estimate required disk space

based on growth� Compute number of disks� Compute number of

enclosures & adapters

� Results:� Start with 4 enclosures

� Add new enclosure when disk count reaches 24

27

Scenario - Server Upgrade:Timeline

• Minimize number of server upgrade points

• Servers upgraded once per 3-month period

• Retire old servers (replace with faster more efficient servers)

28

Scenario - Server Upgrade:Infrastructure - Final Plan

• Final 2-year capacity plan

• Infrastructure supports 5% growth over 24 months

• Server upgrades grouped to reduce downtime (one upgrade per quarter)

29

Scenario - Server Upgrade:Infrastructure Components

15.6 KWh

20.6 KWh

11.2 KWh

13.3 KWh

19.7 KWh

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Apr

2008

May 2

008

Jun 2

008

Jul 2008

Aug 2

008

Sep 2

008

Oct

2008

Nov 2

008

Dec 2

008

Jan 2

009

Feb 2

009

Mar

2009

Apr

2009

May 2

009

Jun 2

009

Jul 2009

Aug 2

009

Sep 2

009

Oct

2009

Nov 2

009

Dec 2

009

Jan 2

010

Feb 2

010

Mar

2010

Apr

2010

At end of 2-years

• 27 Servers 21 kWh / mo

• $150K 42U rack

30

Scenario - Server Upgrade:Monthly Energy Footprint


0

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15,000

20,000

25,000

Apr

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8

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8

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n 2

008

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Aug

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h p

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Not achieving goal of reducing energy footprint

31

Case StudyModeling Scenario Strategy

Server Upgrade





� Dell PE 1850 for all other servers

Virtualization





� Virtualize all other servers on a pool of Dell PE 1950s

� Optimize use of virtual hosts

For each scenario1. Determine infrastructure required to support

workload growth (Capacity Manager)2. Estimate energy footprint3. Calculate cost of IT equipment & energy

32

Scenario - Virtualization:Server Utilization Results

Virtualization: Modeling Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ap

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May 2

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pa

cit

y U

tiliza

tio

n

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9

10

Nu

mb

er o

f Virtu

al H

os

ts

Num VHost(*) CacheApp-AD CacheApp-S CacheApp-S2 CacheFill

Database Image(*) Web(*) VHost(*)

33

Scenario - Virtualization:Infrastructure Components

15.6 KWh

7.7 KWh

6.6 KWh

4.6 KWh

7.2 KWh

Apr

2008

May 2

008

Jun 2

008

Jul 2008

Aug 2

008

Sep 2

008

Oct

2008

Nov 2

008

Dec 2

008

Jan 2

009

Feb 2

009

Mar

2009

Apr

2009

May 2

009

Jun 2

009

Jul 2009

Aug 2

009

Sep 2

009

Oct

2009

Nov 2

009

Dec 2

009

Jan 2

010

Feb 2

010

Mar

2010

Apr

2010

DBWEBWEBWEBWEBWEBWEBWEBWEBWEBWEBWEBWEBCF

CApCApCApImgImgImgImgImgImgImgStoStoStoStoSto

DBVHVHImgImgImgImgImgImgImgStoStoStoSto





DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVHVH

StoStoStoSto

VH

DBVH

VH

StoStoStoSto

VH

StoSto

VH

DBVH

VH

StoStoStoStoStoSto

VH

DBVH

VH

StoStoStoSto

VH

StoSto

VH

Sto

DBVH

VH

StoStoStoSto

VH

StoSto

VH

Sto

DBVH

VH

StoStoStoSto

VH

StoSto

VH

Sto

DBVH

VH

StoStoStoSto

VH

StoSto

VH

Sto

DBVH

VH

StoStoStoSto

VH

StoSto

VH

Sto

DB

VH

VH

StoStoStoSto

VH

StoSto

VH

Sto

VHDB

VH

VH

StoStoStoSto

VH

StoSto

VH

Sto

VHDB

VH

VH

StoStoStoSto

VH

StoSto

VH

Sto

VH

At end of 2-years

• 6 Servers

• 7.7 kWh / mo

• $120K

• 21U rack

34

Example - Storage Upgrade:Replace HDD with SSD

� Replace Database rotating disks with SSDs

�Random workload

�Other stores (Log, Backup) are mostly sequential

� Keep same data throughput and latency levels

�Maintain SLA

� Reduce

�DataCenter footprint

�Power

�Cost (CAPEX, Operating)

� Compare Storage at month 24 only

35


� Replace 4 HDD with one SSD� Similar capacity, will be sufficient to maintain performance

� Reduced space, power & cost (capex)

240120Sequential RD MB/s

10K – 30K300Random RD IOPs

50K – 100K30K – 100KSequential IOPs

800300Cost, $/disk (est)

0.15.8Idle Power, W

2.87.9Active Power, W

2.5” SFF2.5” SFFSize

128 GB36 GBCapacity

SSD (3G SATA)HDD (3G SAS)Feature

36

4 x 2U

HDD Solution


17 HDD Log

17 HDD Log

21 HDD Backup

20 HDD DB1

20 HDD DB2

19 HDD DB1

19 HDD DB2

17 HDD Log

17 HDD Log

21 HDD Backup11 SSD 11 SSD

DB1 DB27 x 2U

SSD Solution

133 HDD

3395 W Active

$68,040 capex

14U rack

55 HDD

22 SSD

1579 W Active

$66,961 capex

8U rack

42%

53%

2%

43%

37

Scenario Comparison:Server Upgrade vs. Virtualization

2142Rack Space (U)

$120,000$150,000Cumulative Cost

7,700 kWh21,000 kWhEnergy Footprint

627Server Count

VirtualizationServer Upgrade

38

Scenario Comparison:Infrastructure Sprawl

Virtualization

Baseline

Server Up

grade

LAN

Database

VHosts

VHosts_1

VHosts_2

VHosts_3

VHosts_4

24 svr, 42U

15.6 kWh/mo

9,700 WIPSb

27 svr, 42U

21 kWh/mo

31,300 WIPSb

6 svr, 21U

7.7 kWh/mo

31,300 WIPSb

39

Scenario - Virtualization:Monthly Energy Footprint

Virtualization: Energy Footprint per Month

0

5,000

10,000

15,000

20,000

25,000

Ap

r 2

00

8

Ma

y 2

00

8

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n 2

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n 2

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v 2

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01

0

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b 2

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r 2

01

0

Ap

r 2

01

0

kW

h p

er

Mo

nth

V:EFP-Storage (kWh) V:EFP-Web (kWh) V:EFP-Image (kWh) V:EFP-CacheApp (kWh)

V:EFP-DB (kWh) V:EFP-CacheFill (kWh) V:EFP-Vhost (kWh) V:EFP-Site (kWh)

Significant reduction:

Energy Footprint (50%)


0

5,000

10,000

15,000

20,000

25,000

Apr

20

08

May 2

00

8

Jun

20

08

Jul 2

008

Au

g 2

00

8

Se

p 2

008

Oct 20

08

No

v 2

008

De

c 2

008

Ja

n 2

00

9

Fe

b 2

009

Mar

20

09

Apr

20

09

May 2

00

9

Jun

20

09

Jul 2

009

Au

g 2

00

9

Se

p 2

009

Oct 20

09

No

v 2

009

De

c 2

009

Ja

n 2

01

0

Fe

b 2

010

Mar

20

10

Apr

20

10

kW

h p

er

Mo

nth



40

Scenario Comparison:Rack Space

Total Rack Space per Month

0

6

12

18

24

30

36

42

Ap

r 2

00

8

Ma

y 2

00

8

Ju

n 2

00

8

Ju

l 2

00

8

Au

g 2

00

8

Se

p 2

00

8

Oct 2

00

8

No

v 2

00

8

De

c 2

00

8

Ja

n 2

00

9

Fe

b 2

00

9

Ma

r 2

00

9

Ap

r 2

00

9

Ma

y 2

00

9

Ju

n 2

00

9

Ju

l 2

00

9

Au

g 2

00

9

Se

p 2

00

9

Oct 2

00

9

No

v 2

00

9

De

c 2

00

9

Ja

n 2

01

0

Fe

b 2

01

0

Ma

r 2

01

0

Ap

r 2

01

0

Ra

ck

Sp

ac

e (

U)

SU:Total Rack Space V:Total Rack Space

Virtualization rack space @ 50%

Server Upgrade requires same rack space at end of

2-year plan

41

Scenario Comparison:Detailed Cumulative Cost

Server Upgrade: Cumulative Monthly Cost

[Energy-IT + Energy-Site + HW-Servers + HW-Storage]

$0

$40,000

$80,000

$120,000

$160,000

Ap

r 2

00

8

Ma

y 2

00

8

Ju

n 2

00

8

Ju

l 2

00

8

Au

g 2

00

8

Se

p 2

00

8

Oct 2

00

8

No

v 2

00

8

De

c 2

00

8

Ja

n 2

00

9

Fe

b 2

00

9

Ma

r 2

00

9

Ap

r 2

00

9

Ma

y 2

00

9

Ju

n 2

00

9

Ju

l 2

00

9

Au

g 2

00

9

Se

p 2

00

9

Oct 2

00

9

No

v 2

00

9

De

c 2

00

9

Ja

n 2

01

0

Fe

b 2

01

0

Ma

r 2

01

0

Ap

r 2

01

0

Virtualization: Cumulative Monthly Cost

[Energy-IT + Energy-Site + HW-Servers + HW-Storage]

$0

$40,000

$80,000

$120,000

$160,000

Ap

r 2

00

8

Ma

y 2

00

8

Ju

n 2

00

8

Ju

l 2

00

8

Au

g 2

00

8

Se

p 2

00

8

Oct 2

00

8

No

v 2

00

8

De

c 2

00

8

Ja

n 2

00

9

Fe

b 2

00

9

Ma

r 2

00

9

Ap

r 2

00

9

Ma

y 2

00

9

Ju

n 2

00

9

Ju

l 2

00

9

Au

g 2

00

9

Se

p 2

00

9

Oct 2

00

9

No

v 2

00

9

De

c 2

00

9

Ja

n 2

01

0

Fe

b 2

01

0

Ma

r 2

01

0

Ap

r 2

01

0

• Identical storage costs

• Virtualization server cost 30% lower

• Energy cost 60% less

42

Looking Ahead

� Emerging technologies are already impacting Green Capacity Planning in the DataCenter� SSD

� Reduce # of disks & power

� Multicore Processors� Higher CPU capacity in same physical & power footprint

� Dynamic Power Management� Manage power of subsystems & components relative to current load

� Virtualization� Combine multiple servers into one server

� Facilities improvements� Creative Cooling

� Fluid cooling, higher temp operation

� Decreasing PUE� Driving PUE very close to 1.0 (lights, fans, chiller)

� Climate-driven locations

43

Energy & PUE Reductions

Reducing PUE doesn’t necessarily mean reduction in energy utilization

First 3 bars all have same PUE, but different monthly KWH

Applying PUE reductions reduce energy utilization

C.S. Base = Case Study Baseline system

C.S. Virt = Case Study system, virtualized, after 24 months of upgrades

Total KWH % of C.S. Base

0%

20%

40%

60%

80%

100%

120%

140%

C.S.

Base

C.S.

Non-Virt

C.S. Virt C.S.

Base

C.S.

Non-Virt

C.S. Virt C.S.

Base

C.S.

Non-Virt

C.S. Virt

Base Base Base Cut

Lights

Cut

Lights

Cut

Lights

Cut

Lights &

Cooling

Cut

Lights &

Cooling

Cut

Lights &

Cooling

% o

f C

ase S

tud

y B

ase

line

, K

WH

/ M

onth

PUE 2.0 PUE 1.9 PUE 1.6

44

Conclusion

� Green Capacity Planning� Energy is a resource

� Introduce Energy Footprint Projection

� Expands traditional capacity planning

� Case Study� Demonstrates the methodology

� Analyzes alternative infrastructures

� Reduces energy footprint with Virtualization

� Value� Actionable metrics

� Quantifies IT and Site energy usage & costs

� Supports responsible decision making

� Bridges the gap between IT & Facilities

45

Primary Reference

This presentation is based upon a 2008 study by:

Amy Spellmann, Optimal Innovations

[email protected]

Richard Gimarc, Hyperformix

[email protected]

Charles Gimarc, LSI Corporation

[email protected]

And a paper presented at the 2008 Computer Measurement Group conference, December 2008. The paper may be downloaded from:

http://www.optimalinnovations.com/pdf/green_capacity_planning.pdf

46

Reference

� [ACC2008] “Data Center Energy Forecast Report, Final Report, July 2008

� [EPA2007] “Report to Congress on Server and Data Center Energy Efficiency”, U.S. Environmental Protection Agency, August 2008

� [KOOM2007] “Estimating Regional Power Consumption By Servers”, Jonathan G. Koomey, LBNL Tech Note

� [SPEC2008] SPECpower_ssj2008, Feb 27, 2008, Dell PowerEdge 2950 III (Intel Xeon E5440)

� [TGG2007] “The Green Grid Data Center Power Efficiency Metrics: PUE and DCiE”, 2007, The Green Grid, Page 6

� [TGG2008] "Five Ways to Reduce Data Center Server Power Consumption“, 2008, Mark Blackburn, The Green Grid, Page 5

� [GCP2008] “Green Capacity Planning: Theory and Practice”, Spellmann Gimarc & Gimarc, CMG 2008

� [GCP-b2008] “Green Capacity Planning”, 2008, C. Gimarc, http://wikibon.org/?c=wiki&m=v&title=Green_Capacity_Planning

Green Capacity Planning: Theory and Practice · 2020-02-20 · future business growth Two year...

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