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E-Science: International Collaborations, Research Networks and Grids

E-Science: International Collaborations, Research Networks and Grids Pan-American Advanced Studies Institute (PASI) Program NSF OISE #0418366 Mendoza, Argentina May 15-21, 2005. Julio Ibarra, PI Heidi Alvarez, Co-PI. 1. Goals and Objectives.

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E-Science: International Collaborations, Research Networks and Grids

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  1. E-Science: International Collaborations, Research Networks and Grids Pan-American Advanced Studies Institute (PASI) Program NSF OISE #0418366 Mendoza, Argentina May 15-21, 2005 Julio Ibarra, PI Heidi Alvarez, Co-PI 1

  2. Goals and Objectives • Understand current issues and challenges involving e-Science collaborations that span beyond national boundaries • Improve our understanding of the role of technology, in particular research networks and Grids, in e-Science • Understand how research faculty, students and practitioners are collaborating in e-Science • Learn from the high-energy physics and astronomy communities about how they use Grids and advanced networking technologies for e-Science • Understand how Grids and advanced networking technologies are being applied for international e-Science collaborations

  3. What is the phenomenon of e-Science? • Experimental science is no longer limited to being conducted in laboratories made of bricks and mortar, and is no longer done in isolation • Science is increasingly being conducted in virtual laboratory environments, it is increasingly collaborative, and increasingly global • In many experimental disciplines, measuring apparatus are in one location, data is captured and reduced at different sites, data analysis is conducted at yet another site, then data is stored at archives located elsewhere • The increasing rates data is generated or collected is impacting how e-Scientists solve problems and coordinate data-intensive work The Very-Long Baseline Interferometry (VLBI) Technique

  4. Why is e-Science Happening? • Discovery requires larger, faster, higher-precision measuring apparatus • Eg., Discovery of new particles or astronomical objects in the Universe • Measuring apparatus have become prohibitively expensive for a single nation to develop • Eg. International Space Station, Radio and Optical telescopes • Technology, in particular high-speed networks, low-cost compute resources, low-cost high-speed disk storage and large-size high-resolution display for visualization of very large data sets, is more affordable and more accessible

  5. Picture ofearthquakeand bridge Sensors More Diversity, New Devices, New Applications Personalized Medicine Picture ofdigital sky Wireless networks Knowledge from Data Instruments

  6. How e-Science has been defined • “e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it.” • Dr John Taylor, Director General of Research Councils, United Kingdom • e-Science refers to large-scale science carried out through distributed global collaborations enabled by networks, requiring access to very large data collections, very-large-scale computing resources and high-performance visualization • NSF CISE Grand Challenges in e-Science Workshop Report • E-Science developed from the field of computational science • Since the late 1980s, computational science became established as a third avenue of scientific discovery, alongside theoretical and experimental methodologies • E-Science goes further by focusing not only on compute-intensive simulations, but also on the remote use of large-scale data and knowledge repositories, scientific instruments and experiments, sensor arrays

  7. What is the Scientific Method? • Observe some aspect of the universe. • Invent a tentative description, called a hypothesis, that is consistent with what you have observed. • Use the hypothesis to make predictions. • Test those predictions by experiments or further observations and modify the hypothesis in the light of your results. • Repeat steps 3 and 4 until there are no discrepancies between theory and experiment and/or observation (source: Wudka, Jose, The Physics 7 Page, University California Riverside, http://phyun5.ucr.edu/~wudka/physics7.html)

  8. The Scientific Method in e-Science • Heavy lines indicate probable high-performance network connections. Dashed lines represent access to archived data, including virtual science, virtual observatories, and other forms of data mining • A measuring engine might be operated by remote control from a Data Capture site • Humans manage most all the process boxes. As a result, there is an information process layer that’s not represented, that is necessary for the exchange of ideas, consultation, collaboration and coordination required to conduct research (source: NSF CISE Grand Challenges in e-Science Workshop Report)

  9. Challenges of Next Generation Science in the Information Age Petabytes of complex data explored and analyzed by 1000s of globally dispersed scientists, in hundreds of teams • Flagship Applications • High Energy & Nuclear Physics, AstroPhysics Sky Surveys: Multi-Terabyte “block” transfers at 1-10+ Gbps • Fusion Energy: Time Critical Burst-Data Distribution; Distributed Plasma Simulations, Visualization, Analysis • eVLBI: Many real time data streams at 1-10 Gbps • BioInformatics, Clinical Imaging: GByte images on demand • NEW “Analysis” Challenge: Provide results to thousands of scientists, with rapid turnaround, over networks of varying capability in different world regions: • Advanced integrated Grid applications rely on reliable, high performance operation of our LANs and WANs Source: Harvey Newman

  10. The Data Deluge Source: DOE Roadmap to 2008 Report

  11. Technology of e-Science

  12. Last updated: 27 April 2005 Abilene International Peering

  13. NSF International Research Network Connections (IRNC)

  14. US - Latin America Year 1 Topology • LILA links reestablish direct connectivity to South America from east and west coasts • Reduces delay reaching sites in Chile and Brazil from the US and Asia-Pacific • Introduces an infrastructure to develop a distributed international exchange points and peering fabrics • Leverages network resources to provide route diversity and high-availability production services 15

  15. TRANSLIGHT 2004 *Lambdas NorthernLight European lambdas to US (red) –10Gb Amsterdam—Chicago –10Gb London—Chicago –10Gb Amsterdam—NYC Canadian lambdas to US (white) –30GbChicago-Canada-NYC –30Gb Chicago-Canada-Seattle US to Europe (grey) –10Gb Chicago—Amsterdam Japan JGN II lambda to US (cyan) –10Gb Chicago—Tokyo European lambdas (yellow) –10Gb Amsterdam—CERN –2.5Gb Prague—Amsterdam –2.5Gb Stockholm—Amsterdam –10Gb London—Amsterdam IEEAF lambdas (blue) –10Gb NYC—Amsterdam –10Gb Seattle—Tokyo CAVEWave/PacificWave (purple) –10Gb Chicago—Seattle –10Gb Seattle—LA—San Diego SurfNet UKLight Japan CERN

  16. Global Lambda Integrated FacilityWorld Map – December 2004 Enabling Communities of Researchers and Communities of Interest

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