Technology for Distributed Collaboration
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Technology for Distributed Collaboration Ian Foster Computation Institute Argonne National Laboratory University of Chicago
description
Technology for Distributed Collaboration. Ian Foster Computation Institute Argonne National Laboratory University of Chicago. www.ci.uchicago.edu. www.ci.anl.gov. In the Next 50 Years, We Must …. Increase energy production by 5, while reducing GHG emissions by 2 or more - PowerPoint PPT Presentation
Transcript of Technology for Distributed Collaboration
Technology forDistributed Collaboration
Ian FosterComputation Institute
Argonne National LaboratoryUniversity of Chicago
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www.ci.uchicago.edu www.ci.anl.gov
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In the Next 50 Years, We Must …
Increase energy production by 5, while reducing GHG emissions by 2 or more
Mitigate and adapt to climate change
Address increasingly drug resistant diseases
Provide meaningful livelihoods for 9B people
Innovation
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We Must Get Smarter …
Maxwell Smart (NBC, 1960s; Warner 2008)
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The Three Dimensions of Smart
Biology
Technology
Culture
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Problem Solvingas “Thinking Aloud”
“What if I try A?”
“I wonder how I do B?”
“What do others know about C?”
“Hey, I’ve just learned how to do D!”
How do I reduce cycle time?
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Thinking Aloud:Reducing Cycle Time
“What if I try A?”
Design, modeling, fabrication tools “I wonder how I do B?”
Wikis, design databases, conversation “What do others know about C?”
Databases, search tools, conversation “Hey, I’ve just learned how to do D!”
Publication, conversation, education
(Distributed) collaboration is a crosscutting theme
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Technologies forDistributed Collaboration
Conversation Post Fedex Telephone Email, IRC, … Instant messaging Videoconference
Immersive MUDs Access Grid Second Life
Data publication FTP, Gopher, … Web Blogs Semantic Web
Federation Collaborative
bookmarking Grid computing Service-oriented
architecture
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GB
/day
Daily 7-Day Average
Provides access to all IPCC data
>150 TB data downloaded
>300 scientific papers written
GB/day
600
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Integrating Data and Computing, on Demand
Public PUMA Knowledge Base
Information about proteins analyzed against ~2 million gene sequences
Back OfficeAnalysis on Grid
Millions of BLAST, BLOCKS, etc., on
OSG and TeraGridNatalia Maltsev et al., http://compbio.mcs.anl.gov/puma2
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Bennett Berthenthal et al., www.sidgrid.org
Social Informatics Data Grid
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caBIG: sharing of infrastructure, applications, and data.
NIH’sCancer Biomedical Informatics Grid
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Medical Educationover Access Grid
Credit: Jonathan Silverstein, U.Chicago
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Access Grid and SARS
Global Communitie
s
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PRAGMA
New communities:
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Community WebsiteSoftware Downloads, User-contributed Content, Hardware Reference, & More
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Globus Downloads Last 24 Hours
Last month
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Lessons Learned
The power of diversity & scale Open Science Grid: 80 sites, 30K CPUs World Community Grid: 700,000 CPUs Access Grid: several thousand nodes Wikipedia, Flickr, CiteULike, Connotea, …
The challenges of heterogeneity Bandwidth, hardware, interests, trust, understanding,
meaning, timezone, … The challenges of scale
Participants, data, computing, ambition Everything is still far too complicated!
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Access Grid:
The Power of Context
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“Thinking Aloud” (for Science or Invention): 10 Year From Now
On-demand access to powerful data, design, analysis, & fabrication resources Service-oriented science & engineering Deep analysis of vast quantities of data Commoditization of design & analysis
Communities of 2, 20, 200, 2K, 2M can self-identify easily within a sea of billions To share information, converse, discover
We understand innovation & collaboration far better than today
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Some Key Challenges Enable smooth scaling in many dimensions
Number of participants (K-, M-, G-persons?) Internet capabilities (0 to Tbit/sec) Physical resources Amount of data (megabytes to exabytes) Complexity of questions asked & answered Degree of trust, shared language, etc.
Integration with the physical world Active sensors Automated experimental protocols Integrate manufacturing and problem solving
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Current Activities
Access Grid 3.0 Conversation, context, scale, ease of use Dozens of sites
Collaborative tagging for scientific data Collaborative creation of data exegesis
Resource federation in virtual organizations Grid protocols and software
Major deployments in development economics
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We Can Contribute to aDemocratization of Science
Personalized manufacturing (FabLab)
Personalized reporting (blogosphere)
Personalized innovation (“Global Knowledge Environment”?)
“So much ingenuity my generation has, and no place to put it” — Charlie Leduff
