I gave this talk on energy use and the information economy at the UC Berkeley Physics of Sustainable Energy Symposium March 8, 2014. It summarizes what I think are the most important issues related to the direct and indirect effects of information technology on energy use.

1. Energy use and the information
economy
Jonathan Koomey, Ph.D.
Research Fellow, Steyer-Taylor Center for Energy Policy
and Finance, Stanford University
http://www.koomey.com
Presented at The Physics of Sustainable Energy III
University of California, Berkeley
March 8, 2014
1

2. Defining “the Internet”
2

3. 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!

4. 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.
1986
1993
2000
2007
2014E
Mobile data
Fixed Internet
Voice

5. …but it’s also physical
5
Photo sources (clockwise from top left): Google. Flickr users Mr. T in DC, digger_90_tristar, geerlingguy, alachia, antonionicolaspina

6. ∑: Growth in IT not necessarily =
Growth in electricity use
6Copyright Jonathan G. Koomey 2013

7. 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

8. 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

9. The big three
• End-user equipment
• Data centers
• Access networks
9

10. End-user Equipment
10

11. 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)

12. Growing installed base of PCs
worldwide
12
Source: IDC 2013 Vernon Turner

13. A key computing trend…
13Source: Koomey et. al.
2011
Energy efficiency of
computing at peak
performance up 100x
every decade!

14. …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!

15. Embedded emissions from
manufacturing
15
Source: 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

16. Data centers
16

17. 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)
Copyright Jonathan G. Koomey 2013 17

18. 18
Electricity Flows in Data Centers
Copyright Jonathan G. Koomey 2013

19. Data centers used
1.3% of global electricity and 2% of US
electricity
in 2010*
*For details see Koomey 2011
19Copyright Jonathan G. Koomey 2013

20. Data center electricity use worldwide
20
Source: 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.

21. Volume servers are dominant
Copyright Jonathan G. Koomey 2013 21
Adapted from data in Koomey 2011

22. Server installed base
22
Source: IDC 2013 Vernon Turner

23. Why asset management is key
Slide courtesy of Winston Saunders, Intel
23Copyright Jonathan G. Koomey 2013

24. 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
24Copyright Jonathan G. Koomey 2013

25. 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
Copyright Jonathan G. Koomey 2013 25

26. 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
Copyright Jonathan G. Koomey 2013 26

27. 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)
RelativeDataCenterEnergyUse(Baseline=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
Increasingoperationalenergyefficiency
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

28. Many efficiency opportunities,
particularly in IT equipment
28
Source: Masanet et al. 2011
Copyright Jonathan G. Koomey 2013

29. 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/colo
installations
Copyright Jonathan G. Koomey 2013 29

30. Annualized costs mostly IT, but
infrastructure costs not trivial
Copyright Jonathan G. Koomey 2013 30
Adapted from data in Koomey et al. 2007

31. 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
Copyright Jonathan G. Koomey 2013 31

32. 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.
Copyright Jonathan G. Koomey 2013

33. 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

34. DSE http://dse.ebay.com
Copyright Jonathan G. Koomey 2013 34

35. Access networks
35

36. Access network bandwidth installed
worldwide in 2010
36
Source: Hilbert and López 2012a and 2012b

37. Access network electricity use,
Sweden 2010
37
Source: Ericsson and TeliaSonera (Malmodin and Lundén et al 2013) with support from CESC, KTH Sweden.

38. System effects of IT
38

39. 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.

40. 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

41. Dematerialization:move bits not atoms
41
Source: Weber et. al. 2010
CO2 emissions for downloads and physical CDs
-80% -40%

42. 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

43. 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

44. 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]

45. Key research issues
45

46. 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
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47. Conclusions
47

48. 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!
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49. References
49

50. 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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51. 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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