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

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

What is the phenomenon of e-Science? l Experimental science is no longer limited to being conducted in laboratories made of bricks and mortar, and is no longer done in isolation l Science is increasingly being conducted in virtual laboratory environments, it is increasingly collaborative, and increasingly global l 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 l 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

Why is e-Science Happening? l Discovery requires larger, faster, higher-precision measuring apparatus  Eg., Discovery of new particles or astronomical objects in the Universe l Measuring apparatus have become prohibitively expensive for a single nation to develop  Eg. International Space Station, Radio and Optical telescopes l 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

Instruments Picture of digital sky Knowledge from Data Sensors Picture of earthquake and bridge Wireless networks Personalized Medicine More Diversity, New Devices, New Applications

How e-Science has been defined l “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 l 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 l 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 l 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

What is the Scientific Method? Observe some aspect of the universe. Observe some aspect of the universe. Invent a tentative description, called a hypothesis, that is consistent with what you have observed. Invent a tentative description, called a hypothesis, that is consistent with what you have observed. Use the hypothesis to make predictions. Use the hypothesis to make predictions. Test those predictions by experiments or further observations and modify the hypothesis in the light of your results. 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, 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,

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)

Challenges of Next Generation Science in the Information Age l Flagship Applications  High Energy & Nuclear Physics, AstroPhysics Sky Surveys: Multi-Terabyte “block” transfers at 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 l NEW “Analysis” Challenge: Provide results to thousands of scientists, with rapid turnaround, over networks of varying capability in different world regions: l Advanced integrated Grid applications rely on reliable, high performance operation of our LANs and WANs Petabytes of complex data explored and analyzed by 1000s of globally dispersed scientists, in hundreds of teams Source: Harvey Newman

The Data Deluge Science Areas Today End2End Throughput 5 years End2End Throughput 5-10 Years End2End Throughput Remarks High Energy Physics 0.5 Gb/s 100 Gb/s 1000 Gb/s high bulk throughput Climate (Data & Computation) 0.5 Gb/s Gb/s N x 1000 Gb/s high bulk throughput NanoScience - Spallation Neutron Source Not yet started 1 Gb/s 1000 Gb/s + QoS for control channel remote control and time critical throughput Fusion Energy Gb/s (500 MB/s burst) Gb/s (500MB/ 20 sec. burst) N x 1000 Gb/s time critical throughput Astrophysics Gb/s (1 TBy/week) N*N multicast 1000 Gb/s computational steering and collaborations Genomics Data & Computation Gb/s (1 TBy/day) 100s of users 1000 Gb/s + QoS for control channel high throughput and steering Source: DOE Roadmap to 2008 Report

Technology of e-Science

Last updated: 27 April 2005 Abilene International Peering

NSF International Research Network Connections (IRNC)

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

European lambdas to US (red) –10Gb Amsterdam—Chicago –10Gb London—Chicago –10Gb Amsterdam—NYC Canadian lambdas to US (white) –30Gb Chicago-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 TRANSLIGHT 2004 *Lambdas NorthernLi ght UKLight CERN Japan SurfNet

Global Lambda Integrated Facility World Map – December 2004 Enabling Communities of Researchers and Communities of Interest