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 ]
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