This document discusses emerging technologies and trends that will impact research and education networks (NRENs) going forward. It identifies 12 disruptive technologies like mobile internet, cloud computing, and 3D printing that are forecast to greatly impact the global economy. Big data is noted as a primary driver of these changes. The document also discusses implications of globalization, team-based science, and big data - including that NRENs may need to work together more as a global research and education network (GREN) and use software-defined networking (SDN) to help configure networks for different applications and users. It speculates that future NREN nodes could function like content distribution networks (CDNs) and that more computation may occur within

1. NREN 3.0STEPHEN WOLFF
Internet2
JUNE 2013

2. • Mobile Internet
• Automation of knowledge work
• Internet of things
• Cloud technology
• Advanced robotics
• Autonomous/near-autonomous
vehicles
• Next-generation genomics
• Energy storage
• 3-D printing
• Advanced materials
• Advanced oil/gas exploration,
recovery
• Renewable energy
[ 2 ]
Twelve disruptive technologies
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“Disruptive Technologies: Advances that will transform life, business, and the global economy”
McKinsey Global Institute, 2013
These technologies are forecast to account for $33 trillion of the global economy in 2025

3. [ 3 ]
Big Data is the primary driver
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Familiar numbers:
• Major genome sequencing
centers @ 2 PB/year
• LSST @ 10 PB/year
• LHC @ 30 PB/year
• SKA @ 1 EB/day
• …

4. • Continued rise of “team” science
“To a very considerable extent statistics and applied mathematics and parts of
pure mathematics have become ‘group’ science requiring bodies of graduate
students, postdocs and computing resources…”
http://www.nsf.gov/attachments/124926/public/DMS_Name_Change_Committee_Report_Final_4-1-12.pdf
• One Culture. Computationally Intensive Research in the Humanities and
Social Sciences, Christa Williford & Charles Henry, CLIR, June 2012
• Internet2/Microsoft workshop on CI for social science February 2013
• IEEE Workshop on Big Humanities October 2013
[ 4 ]
Then there’s the long tail
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5. Globalization of scientific research, e.g.:
• LHC involved scientists from 36 countries
• Annual funding for India-US collaborative research exceeds
$2B, and there are more than 360 projects funded at
$250,000 or more
• Annual funding for China-US collaborative research exceeds
$1B, and there are more than 450 projects funded at
$100,000 or more
[ 5 ]
…and ‘teams’ are not all local
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6. Globalization and democratization of higher education –
• Remote campuses
• Classical distance education
• MOOCs
[ 6 ]
Nor is higher education…
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7. [ 7 ]
US NREN speed vs. Moore’s Law
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1985 1990 1995 2000 2005 2010
YEAR
10 kb/s
100
1 mb/s
10
100
1 Gb/s
10
100 Gb/s

8. [ 8 ]
OAM multiplexing
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9. [ 9 ]
OAM multiplexing
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• Willner et al USC
• Nearly 100 b/s/Hz
BUT
• You can’t beat
Shannon

10. Moving “Big Data” has led to fundamental advances in the
understanding and design of networks
• Van Jacobson and Berkeley UNIX source (1988)
• Matt Mathis and Web100 (2003)
• Science DMZ (ESnet, 2009)
[ 10 ]
Big Data
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11. Using “Big Data” has led to fundamental advances in
science and in analytics
• “Traditional” science, with Big Data (e.g., LHC)
• The Fourth Paradigm – learning algorithms, coping with
velocity, unstructured data, visualization,…
[ 11 ]
Big Data
June 11, 2013 © 2013 Internet2

12. • Globalization ⇒ No NREN is an island
• Team science ⇒ (possibly virtual) team networks for
virtual communities
• Big Data ⇒ More than just bigger pipes
[ 12 ]
Implications/consequences

13. • Globalization ⇒ It’s time to engineer global R&E
connectivity: from NRENs to a GREN
• Team science ⇒ A one-size-fits-all IP network is not
enough. SDN (e.g., OpenFlow, OSCARS) will help users
and applications configure the network
• Big Data ⇒ Re-architecting (Science DMZ), tuning
(Web10G), measuring (perfSONAR)
[ 13 ]
Further implications

14. • Data = “content”
• Future NREN node?
• CDN/NDN
– via SDN?
[ 14 ]
…and a speculation
Source: D. Comer

15. • Growing number of instances where the data are too
numerous to move
• Instead, move the computation to the data
• Computation in place
– AWS, Microsoft Azure, cloud computing generally…
• Map-reduce and Hadoop
– Widely applicable and successful programming model
– Not well adapted to typical HPC architectures
[ 15 ]
The Black Swan

16. • All the data are not – and cannot – be in one place
– 10 EB of spinning disk ≈ 2.4 Gwatts
not counting backups or redundancy measures"
• “Computation in place” => GREN as backplane
[ 16 ]
Distributed data

17. • Computers, datastores, and networks are all programmable
• Computational models / multicomputer architectures, network
architectures, and storage architectures are converging
disciplines
therefore
• NREN engineers and operators must acquire added skills.
• The need is for support staff who are versed in computational
_______, and able to program everything (see above)
[ 17 ]
Convergence

18. • “Big data” and the growth of team science are bringing in to our
community researchers with little background in computing
beyond their laptops.
• We are faced with a group of scientists who are “…analyzing data
they did not collect, using software they did not write, on
computers they do not own.” – Myron Livny
• Not only are Renaissance thinkers needed for support staff, we
must begin to hold scientific software to the same standards as
publications. cf. Joppa et al, “Troubling Trends in Scientific
Software Use”, Science, v 340, pp 814-815, 2013/05/17
[ 18 ]
Bootnote
Bootnote

19. NREN 3.0STEPHEN WOLFF
[ 19 ]
swolff@internet2.edu
June 11, 2013 © 2013 Internet2
Thank
you!