Koomey's talk on energy use and the information economy at the UC Berkeley Physics of Sustainable Energy Symposium March 8, 2014

Energy use and the information economy

Jonathan Koomey, Ph.D.Research Fellow, Steyer-Taylor Center for Energy Policy

and Finance, Stanford Universityhttp://www.koomey.com

Presented at The Physics of Sustainable Energy IIIUniversity of California, Berkeley

March 8, 2014

1

Defining “the Internet”

2

The big picture view

3Source: Ericsson and TeliaSonera (Malmodin and Lundén et al 2013)

with support from CESC, KTH Sweden

Key components

• Data centers

• Core network

• Access networks

• End-user

communications

equipment

• End-user computing

equipment

Lots of complexity here!

The Internet is data…

4

Source: 1986 to 2007 adapted from Hilbert et. al. 2011; 2014 extrapolated using Cisco VNI data compiled at http://en.wikipedia.org/wiki/Internet_traffic

Doubling time 1986 to 2014 = 3 years, doubling time 2000 to 2014 = 1.5 years.

19861993

2000

2007

2014E

Mobile data

Fixed Internet

Voice

…but it’s also physical

5Photo sources (clockwise from top left): Google. Flickr users Mr. T in DC, digger_90_tristar, geerlingguy, alachia, antonionicolaspina

∑: Growth in IT not necessarily = Growth in electricity use

6Copyright Jonathan G. Koomey 2013

Two opposing factors

• Equivalently

• So if Computations/Year goes up faster than Computations/kWh, then total kWh goes up!

Copyright Jonathan G. Koomey 2013 7

What matters most…

8Source: Ericsson and TeliaSonera (Malmodin and Lundén et al 2013)

with support from CESC, KTH Sweden. Data are for Sweden, circa 2010.

These are key

The big three

• End-user equipment

• Data centers

• Access networks

9

End-user Equipment

10

End user equipment

11

Computing– Desktops and local servers

– Laptops

– Tablets

Communications– Phones

– Wireless routers

– Set-top boxes

– Switches

Display– Computer monitors

– TVs (IP connected)

Ultra low-power computing/sensors (small but growing)

Growing installed base of PCs worldwide

12Source: IDC 2013 Vernon Turner

A key computing trend…

13Source: Koomey et. al.

2011

Energy efficiency of computing at peak performance up 100x every decade!

…led to the rise of tablets and mobile phones

14

Source: IDC (http://www.idc.com/getdoc.jsp?containerId=prUS24129713) Source: Hilbert and López 2012a and 2012b

Tablet shipments =

desktops in 2012!

Embedded emissions from manufacturing

15Source: Koomey et. al. 2013

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

Server (Mac Mini OS X server)

Laptop computer (Macbook Pro 13")

Smart Phone (iPhone5)

NAND Flash memory - 1 GB

Share of CO2 emissions

Production Operation

0 200 400 600 800 1000 1200

Life Cycle CO2 emissions (kg)

Percentage contributions Absolute emissions

Data centers

16

Kinds of data centers

• Hyperscale (e.g., Google, Facebook, Microsoft, eBay, others)

• Enterprise or “in-house” (vast majority)

– Conventional

– Internal cloud (similar to hyperscale)

• Co-location (my facility, your IT)

• High Performance Computing (special case–batch jobs, very high utilization)


18

Electricity Flows in Data Centers

Copyright Jonathan G. Koomey 2013

Data centers used1.3% of global electricity and 2% of US

electricityin 2010*

*For details see Koomey 2011


Data center electricity use worldwide

20Source: Koomey 2011. Graph shows worldwide numbers. For the US, the range for data centers in 2010 was 1.7 to 2.2% of the total.

N.B. Infrastructure in this slide refers to cooling, fans, pumps, and power distribution inside data centers.

Volume servers are dominant


Adapted from data in Koomey 2011

Server installed base

22

Source: IDC 2013 Vernon Turner

Why asset management is key

Slide courtesy of Winston Saunders, Intel23Copyright Jonathan G. Koomey 2013

Idle power improving: Server power curves (via Intel)

• Usage Driven

• Variable Utilization

• Proportional Energy Use

• Optimized Efficiency

• Technology Scope:

• CPU and Memory

• Power Delivery, Fans, etc.

• Instrumentation

Approaching “Ideal” Server Behavior

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark* and MobileMark*, are measured using specific

computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you

in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Configurations: Dual Socket Server. For full configuration information, please see

backup. For more information go to http://www.intel.com/performance

Xeon™ 5160

Xeon™ E5-2660

2012

2006

Data from spec.org

Source: Winston Saunders, Intel


http://www.intel.com/performance

A key metric you will encounter

• PUE = Power Utilization Effectiveness

• PUE =

• Measures infrastructure efficiency but says nothing about IT efficiency

• Typical PUEs

– Hyperscale/modular: 1.05 to 1.15

– New enterprise DCs: 1.25 to 1.5

– Existing enterprise DCs: 1.8 to 2.0


A broader view

• DC GHG emissions affected by 3 factors

– Infrastructure efficiency (PUE)

– IT efficiency

– Emissions intensity of electricity

• Best to focus on value, costs, and emissions per computation, not narrow efficiency metrics

• More computations means higher total business value, lower costs per computation, and higher profits


PUE isn’t everything

27

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Relative Data Center GHG Emissions (Baseline = 1)

Re

lati

ve D

ata

Ce

nte

r En

ergy

Use

(B

ase

line

= 1

) [6]

[5]

[4]

[3]

[1]

[2]

PUE=1.8, minimal IT efficiency

PUE=1.5, minimal IT efficiency

PUE=1.3 (free cooling, warm climate), minimal IT efficiency

PUE=1.1 (free cooling, cool climate), minimal IT efficiency

Decreasing electric power CO2 intensity

Incr

easi

ng

op

erat

ion

al e

ner

gy e

ffic

ien

cy

High energy, high carbon region

High energy, low carbon region

Low energy, low carbon region

Baseline data center powered by coal:- Energy use = 92 GWh/yr- GHG emissions = 89 kt CO2e/yr

PUE=1.1, maximal IT efficiency

PUE=1.8, maximal IT efficiency

[D] Coal(0.96 kt CO2e/GWh)

[C] U.S. average electricity(0.6 kt CO2e/GWh)

[B] Natural gas SOFC(0.35 kt CO2e/GWh)

[A] Renewables(~0.02 kt CO2e/GWh)

Source: Masanet et al. 2013

Many efficiency opportunities, particularly in IT equipment

28

Source: Masanet et al. 2011


Energy-related advantages of hyperscale/cloud computing

• Low PUE

• Diversity of users

• Economies of scale

• Flexibility (because of abstraction/virtualization)

• Easier provisioning for outside users

∑: Costs and energy use per computation much lower than conventional enterprise/coloinstallations


Annualized costs mostly IT, but infrastructure costs not trivial


Adapted from data in Koomey et al. 2007

Capital costs to build data center infrastructure

• From Uptime Institute (Stanley and Schafer 2012)

– Lowest $5M/MW of IT load

– Mean $15M/MW of IT load

– Highest: $25 M/MW of IT load

• Modular/prefabricated solutions typically cheaper

– Mean: < $10M/MW


What’s 1 W of IT savings worth?

32

Source of data: Koomey 2012. Infrastructure capital savings apply to new construction or existing facilities that are power/cooling constrained. Those savings total $8.6M/MW for cloud facilities and $15M/MW for others, from Uptime institute. PUE = 1.1, 1.5, and 1.8 for Cloud, New, and Existing data centers, respectively. Electricity price =$0.039/kWh for cloud facilities and $0.066/kWh for new/existing data centers. All costs in 2012 dollars.


Data center lessons

• Biggest inefficiencies in enterprise data centers (cloud providers much better)

• Just adopting best practices will save 80+%

• Biggest impediments to efficiency are institutional, not technical

• IT efficiency most important, followed by infrastructure efficiency and sourcing of low-carbon electricity (embedded emissions not so important)

33

DSE http://dse.ebay.com


Access networks

35

Access network bandwidth installed worldwide in 2010

36Source: Hilbert and López 2012a and 2012b

Access network electricity use, Sweden 2010

37Source: Ericsson and TeliaSonera (Malmodin and Lundén et al 2013) with support from CESC, KTH Sweden.

System effects of IT

38

Is the Internet an energy hog?

39

0%

1%

2%

3%

4%

5%

Electricity

consumption

Energy

consumption

CO2 emissions GDP

1992-1996 1996-2000

Annual percentage growth

N.B., the amount of data flowing through the Internet grew at about 100%/year in the late 1990s

Source: Koomey et al. 2002.

System effects of IT

• Dematerialization (move bits not atoms)

– CDs vs downloads for music

• Big systems optimization

– Smart parking sensors reduce traffic

• Enabling structural change

– Flatter, nimbler organizations

40

Dematerialization:move bits not atoms

41Source: Weber et. al. 2010

CO2 emissions for downloads and physical CDs

-80% -40%

Big systems optimization: Smart parking

42Source: Mark Noworolski, Streetline Networks

Motes use <400μW on

Average. For LA, with 40,000

parking spots, that implies

total mote power of about

15W. Mote technology is from

Dust Networks

Structural change: Nimble organizations

• IT enables business process redesign, improving efficiency across the board

• Example:

– Gave suppliers access to POS and inventory data, as well as company forecasts

– Pioneered aggressive use of RFID

– Improved the flow of supplies and finished goods

– The result: Better coordination of suppliers with Walmart’s needs, plus much lower distribution costs

43

Suggested reading

44

Brynjolfsson, Erik, and Andrew McAffee. 2014. The Second Machine Age: Work, Progress, and

Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton & Company.

[http://amzn.to/1gYHEGk]

Key research issues

45

Key research issues

• Need recent data on electricity use and potential savings

• Need more system efficiency case studies

• Need more and better automated reporting of– Energy use

– User behavior

• Average (fixed) vs marginal (variable) energy use– Most devices have high fixed energy use

– Be careful to distinguish average vs marginal effects

• Address rise of machine to machine communications

46

Conclusions

47

Conclusions

• Popular preoccupation with electricity used by Internet-related systems is misplaced– Almost certainly <10% of total electricity, but not well

characterized

– End-user devices important, but most can’t be clearly allocated to “the Internet”

• System effects potentially much more important than direct electricity use– IT affects the other 90% of electricity plus all the fuels

• Updated data needed!

48

References

49

References• Brynjolfsson, Erik, and Lorin M. Hitt. 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance."

Journal of Economic Perspectives. vol. 14, no. 4. Fall. pp. 23-48.

• Hilbert, Martin, and Priscila López. 2011. "The World's Technological Capacity to Store, Communicate, and Compute Information." Science. vol. 332,

no. 6025. April 1. pp. 60-65.

• Hilbert, Martin, and Priscila López. 2012a. "Info Capacity| How to Measure the World’s Technological Capacity to Communicate, Store and Compute

Information? Part I: Results and Scope." International Journal of Communication. vol. 6, pp. 956-979.

[http://ijoc.org/ojs/index.php/ijoc/article/view/1562/742]

• Hilbert, Martin, and Priscila López. 2012b. "Info Capacity| How to Measure the World’s Technological Capacity to Communicate, Store and Compute

Information? Part II: Measurement Unit and Conclusions." International Journal of Communication. vol. 6, pp. 936-955.

[http://ijoc.org/ojs/index.php/ijoc/article/view/1563/741]

• Koomey et al. 2002. "Sorry, wrong number: The use and misuse of numerical facts in analysis and media reporting of energy issues." In Annual

Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. (also LBNL-

50499). pp. 119-158.

• 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>.

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References (continued)• Koomey, Jonathan G., Stephen Berard, Marla Sanchez, and Henry Wong. 2011. "Implications of Historical Trends in The Electrical Efficiency of

Computing." IEEE Annals of the History of Computing. vol. 33, no. 3. July-September. pp. 2-10.

[http://www.computer.org/csdl/mags/an/2011/03/man2011030046-abs.html]

• Koomey, Jonathan. 2011. Growth in data center electricity use 2005 to 2010. Oakland, CA: Analytics Press. August 1.

[http://www.analyticspress.com/datacenters.html]

• Koomey, Jonathan G. 2012. The Economics of Green DRAM in Servers. Burlingame, CA: Analytics Press. November 2.

[http://www.mediafire.com/view/uj8j4ibos8cd9j3/Full_report_for_econ_of_green_RAM-v7.pdf]

• Koomey, Jonathan G., H. Scott Matthews, and Eric Williams. 2013. "Smart Everything: Will Intelligent Systems Reduce Resource Use?" The Annual

Review of Environment and Resources.vol 38. October. pp. 311-343. [http://www.annualreviews.org/doi/abs/10.1146/annurev-environ-021512-110549].

• Masanet, Eric R., Richard E. Brown, Arman Shehabi, Jonathan G. Koomey, and Bruce Nordman. 2011. "Estimating the Energy Use and Efficiency

Potential of U.S. Data Centers." Proceedings of the IEEE. vol. 99, no. 8. August.

• Masanet, Eric, Arman Shehabi, and Jonathan Koomey. 2013. "Characteristics of Low-Carbon Data Centers." Nature Climate Change. July. Vol. 3, No.

7. pp. 627-630. [http://dx.doi.org/10.1038/nclimate1786]

• Malmodin, Jens, Dag Lundén, Åsa Moberg, Greger Andersson, and Mikael Nilsson. 2013. "Life cycle assessment of ICT networks–carbon footprint and

operational electricity use from the operator, national and subscriber perspective." Submitted to The Journal of Industrial Ecology. March 8.

• Traub, Todd. 2012. "Wal-mart used technology to become supply chain leader." In Arkansas Business. July 2.

[http://www.arkansasbusiness.com/article/85508/wal-mart-used-technology-to-become-supply-chain-leader]

• Weber, Christopher, Jonathan G. Koomey, and Scott Matthews. 2010. "The Energy and Climate Change Impacts of Different Music Delivery Methods."

The Journal of Industrial Ecology. vol. 14, no. 5. October. pp. 754–769. [http://dx.doi.org/10.1111/j.1530-9290.2010.00269.x]

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http://www.annualreviews.org/doi/abs/10.1146/annurev-environ-021512-110549