In the academic research community we've made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of large quantities of data. Exploding data volumes and powerful simulation tools mean that most researchers will soon require similar capabilities, but they often do not have the resources or expertise to build and maintain the necessary IT infrastructure. Faced with a similar problem in industry, companies have adopted the Software-as-a-Service (SaaS) model to "free" themselves from IT complexity. We see the same shift occurring in the academic research world over the next decade - indeed, many of us use SaaS services such as Google Docs and Dropbox on a daily basis as an integral part of our research workflow. Here we describe a vision for the next generation of research cyberinfrastructure, and work that the University of Chicago has embarked on to further empower investigators and enable them to access new capabilities beyond the boundaries of their campus.

1. SaaS and the
Transformation of
Research
Vas Vasiliadis
vas@uchicago.edu
ci.uchicago.edu

2. Urban Science

3. Thank you to our sponsors!
U.S. DE PARTME NT OF
ENERGY

4. Higgs discovery “only possible
because of the extraordinary
achievements of …grid
computing”
Rolf Heuer, CERN DG

5. 25PB per year
8,000 scientists worldwide

6. 1PB in last experiment
800 scientists worldwide

7. 1.2 PB of climate data
Delivered to 23,000 users

8. We have exceptional
infrastructure for the 1%

9. What about the 99%?

10. Most labs have limited resources
NSF grants in 2007
< $350,000
80% of awards
50% of grant $$
$1,000,000
$100,000
$10,000
$1,000
2000 4000 6000 8000
Bryan Heidorn

11. 57.7%

12. 2012 Faculty Burden Survey, National Academies
40
45
50
55
60
65
< $50K $50-99K $100-199K $200-299K $300-499K $500-999K $1-3M > $3M
Federal Funding Amount
ActiveResearchTime(%) Active Research Time vs.
Federal Funding Amount

13. Potential economies of scale
Small laboratories
– PI, postdoc, technician, grad students
– Estimate 10,000 across US research community
– Average ill-spent/unmet need of 0.5 FTE/lab?
+ Medium-scale projects
– Multiple PIs, a few software engineers
– Estimate 1,000 across US research community
– Average ill-spent/unmet need of 3 FTE/project?
= Total 8,000 FTE: at ~$100K/FTE => $800M/yr
(If we could even find 8,000 skilled people)
Plus computers, storage, opportunity costs, …

14. Is there a better way to deliver
research cyberinfrastructure?
Frictionless
Affordable
Sustainable

15. Commercial startups
as “role models”

16. My Shiny
New Startup

17. “Frictionless”
Great User Experience
+
High performance
(but invisible) infrastructure

18. SaaS is transformational for…
Researchers

19. A simple problem
• “Transfers often take longer than expected
based on available network capacities”
• “Lack of an easy to use interface to some of the
high-performance tools”
• “Tools [are] too difficult to install and use”
• “Time and interruption to other work required to
supervise large data transfers”
• “Need data transfer tools that are easy to
use, well-supported, and permitted by site and
facility cybersecurity organizations”
Excerpts from ESnet reports

20. Exemplar: APS Beamline 2-BM
X-Ray imaging, tomography, ~few µm to
30nm resolution
Currently can generate
>100TB per day
<1GB/s data rate; ~3-
5GB/s in 5-10 years

21. Transforming data acquisition
Current
• Experimental parameters
optimized manually
• Collected data combined
with visual inspection to
confirm optimal condition
• Data reconstructed and sent
to users via external drive
• User team starts data
reduction at home institution

22. Transforming data acquisition
Envisaged
• Experimental parameters
optimized automatically
• Collected data available to
optimization programs
• Data are automatically
reconstructed, reduced, an
d shared with local and
remote participants
• User team leaves the APS
with reduced data
Current
• Experimental parameters
optimized manually
• Collected data combined
with visual inspection to
confirm optimal condition
• Data reconstructed and sent
to users via external drive
• User team starts data
reduction at home institution

23. Facility data
acquisition
Research Data Management
as a Service
Globus transfer
service
Reduced
data
Analysis/Shar
ingGlobus sharing
service
Globus data
publication service*
* In development

24. 730GB
90 minutes
“…frees up my time to do more creative work rather than
typing scp commands or devising scripts to initiate and
monitor progress to move many files.”
Steven Gottlieb, Indiana University

25. San Diego to Miami
1 click
20 minutes
“Twenty minutes instead of sixty one hours.
Globus makes OLAM global climate
simulations manageable.”
Craig Mattocks, University of Miami

26. Early adoption is encouraging

27. 15,327
endpoints

28. 182*
daily users
*30-day average

29. 41.8PB

30. 2B files

33. Other innovative science
SaaS projects

34. “Affordable”
Competitive TCO
at
Modest scale

35. A time of disruptive change

36. A time of disruptive change

37. Will data kill genomics?

38. “We are close to having a $1,000
genome sequence, but this may be
accompanied by a$1 million
interpretation.”
Bruce Korf M.D.,
Past President, American College of Medical Genetics
Will data kill genomics?

39. globus
genomics
Flexible, scalable, affordabl
e genomics analysis for all
biologists

40. +
Data management
SaaS
Next-gen sequence
analysis pipelines
+
Scalable IaaS

41. Exome: $3 – $20
Whole Genome: $20 – $50
RNA-Seq: <$5
Alternatives are at 10-20x

42. Affordable scalability
350K Core hours in last 6 months

43. Dobyns Lab
Exome analysis
20x speed-up
Next: 50x

44. Cox Lab
Consensus variant calling
134 samples; 4 days
<0.01% Mendel error rate
Next: 13,000 samples

45. Another Example: DTI Pipelines
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
CostperSubject($)
On-Demand Spot (Low) Spot (High)

46. SaaS is transformational for…
Researchers
Resource Providers

47. installers  brokers

48. Cede (some) control
Evolve financial models
Adapt institutional policies
Become a lawyer!

49. developers  integrators
GSI-OpenSSH

50. A platform for integration

51. A platform for integration

52. A platform for integration

53. administrators  curators
(of the user experience)
1 : 1 : 0
UX : Dev : Ops

54. We are a non-profit service
provider to the non-profit
research community

55. Our challenge:
Sustainability
We are a non-profit service
provider to the non-profit
research community

56. “Affordable” and “Sustainable”?
Either
High-priced commercial software (with
generally higher levels of quality)
Or
Free, open source software (with generally
lower levels of quality)
Is there a happy medium?

57. Industry and economics themes
• Matlab: Commercial closed-source software.
Sustainability achieved via license fees.
• Kitware: Commercial open source software.
Sustainability achieved via services (mostly gov.?).
• DUNE: Community of university and lab people, with
some commercial involvement.
• MVAPICH: Open source software. University team.
Sustainability by continued fed. funding, some industry.

58. Globus: Subscriptions
Globus Provider plans
(globus.org/provider-plans)
Globus Plus
(globus.org/plus)

59. To provide more capability for
more people at substantially
lower cost by creatively
aggregating (“cloud”) and
federating (“grid”) resources
Our vision for a 21st century
discovery infrastructure