This document discusses the growing issue of large quantities of data from scientific research and simulations. It notes that data volumes are doubling every 9 months for genomic sequencing and that various scientific projects are now generating petabytes and exabytes of data. Current solutions for data management and analysis are often ad-hoc and inadequate for small and medium scale research. The document advocates for a national research cyberinfrastructure strategy in Canada that could provide standardized services and leverage partnerships with international organizations and commercial cloud providers to support scientists in dealing with large and complex research data. It raises questions about leadership and roles for universities, research organizations, and government in developing solutions.

1. Drowning in data
• The need to deal with
and benefit from large
quantities of data is not
a new concept: it has
been noted in many
policy
reports, particularly in
the US and UK, over the
past several years.
Source: Ian Foster, UoChicago
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2. The Data Deluge
2004: 36 TB
2012: 2,300 TB
Genomic sequencing output x2 every Climate
9 month model intercomparison
project (CMIP) of the IPCC
MACHO et al.: 1 TB
Palomar: 3 TB
2MASS: 10 TB
GALEX: 30 TB
Sloan: 40 TB
Pan-STARRS:
40,000 TB
1330 molec. bio databases
Nucleic Acids Research (96 in Jan 2001) Source: Ian Foster, UoChicago

3. Big science has achieved big successes
OSG: 1.4M CPU-
hours/day, >90 sites, >3000
users,
>260 pubs in 2010
LIGO: 1 PB data in last science
run, distributed worldwide
Robust production solutions
Substantial teams and expense
Sustained, multi-year effort
Application-specific solutions,
built on common technology
ESG: 1.2 PB climate data
delivered to 23,000 users; 600+ pubs
Source: Ian Foster, UoChicago

4. Growth in sensor networks and Citizen
Science
Glacier Tracking
Real Time Health Monitoring
Smart Trash
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5. NSF Vision
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6. Critical Factors
Source: NSF 6

7. But small & medium science in Canada is
struggling
More data, more complex data
Ad-hoc solutions
Inadequate software, hardware
Data plan mandates
Source: Ian Foster, UoChicago

8. Time-consuming tasks in science
• Communicate with
• Run experiments colleagues
• Collect data • Publish papers
• Manage data • Find, configure, install
• Move data relevant software
• Acquire computers • Find, access, analyze
• Analyze data relevant data
• Run simulations • Order supplies
• Compare experiment • Write proposals
with simulation • Write reports
• Search the literature • … Source: Ian Foster, UoChicago
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9. SaaS services in action: The XSEDE
vision
Academic institution = Standard
interface
XUAS
Globus Online: Hosted persistent services
User Team Catalog Transfer Compute ...
2
InCommon
... Open
Commercial Data Science
XSEDE service provider provider provider Grid
Source: Ian Foster, UoChicago
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10. The real cost of campus computing
• HPC represents 15-20% of campus
electrical energy at many Canadian
universities*
• Closet clusters consume 5-10% of
campus electricity*
• Universities collectively spending
millions of dollars on capital cost
and electrical energy of computing
Belady, C., “In the Data Center, Power and Cooling Costs
More than IT Equipment it Supports”, Electronics Cooling
Magazine (February 2007)
Source: Christian Belady
* Studies undertaken by CANARIE of 4 universities: UBC, Dalhousie, Ottawa U, UoAlberta

11. Research Computing Pyramid
Compute, compute, compute
Petascale/Exascale/… 102
National HPC infrastructure
Capable Users
Compute Canada
University HPC infrastructure
Role for cloud
computing Closet clusters
Mobile/Desktop 109
computing
Data, data, data
Source: Dan Reed, PCAST 11

12. USA & Europe programs -commercial
clouds to support research
• US Government $200 million “Big Data for Research and Discovery”
research universities, government labs and commercial cloud
providers
– For example 1000 person genome project stored on Amazon with free
access to researchers
– Grants available to researcher to use Amazon tools to undertake
computation
• European public –private clouds for research partnership –
“European Cloud Partnership”
– CERN, European Space Agency, European Molecular Laboratory plus
several Internet companies
• Network organizations in USA, UK , Netherlands etc are brokering
commercial cloud services for research and education to
significantly reduce costs
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13. Other Canadian initiatives
• CANARIE + Compute Canada
– “Integrated Digital Infrastructure”
– Integrating networks and HPC
• Research directions being
determined by the
infrastructure?
• Workshop in Saskatoon in
June
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14. Questions for attendees
1. Should Canada pursue a research cyber-infrastructure and/or Big Data strategy?
2. Do we need an organization or leadership council to promote a cyber-
infrastructure or Big Data strategy in Canada?
3. Given Canada is so far behind, should we partner with international groups such
as XSEDE, NeCTAR, etc
4. Should we focus on those who need the most help – small and medium science
in Canada?
5. Who should lead cyber-infrastructure in Canada? Researchers, infrastructure
providers, funding councils, VPRs, CIOs, Government?
6. Is it the role of universities to operate 1 MW power plants and massive compute
facilities that are identical to commercial facilities?
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