Frontiers in Research and Infrastructure in CISE
Jeannette M. WingAssistant Director
Computer and Information Science and Engineeringand
President’s Professor of Computer ScienceCarnegie Mellon University
Coalition for Academic Scientific ComputationArlington, VA29 April 2009
NSF
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Stewardship for the Field NSF support as a percentof total federal supportof academic basic research
Foundation-wide Initiative
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CDI: Cyber-Enabled Discovery and Innovation
• Paradigm shift– Not just computing’s metal tools (transistors and wires) but also our mental
tools (abstractions and methods)
• It’s about partnerships and transformative research.– To innovate in/innovatively use computational thinking; and– To advance more than one science/engineering discipline.
• FY08: $48M invested by all directorates and offices– 1800 Letters of Intent, 1300 Preliminary Proposals, 200 Final Proposals, 36
Awards
Computational Thinking for Science and Engineering
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Range of Disciplines in CDI Awards
• Aerospace engineering• Atmospheric sciences• Biochemistry• Biophysics• Chemical engineering• Communications science and engineering• Computer science• Geosciences• Linguistics• Materials engineering• Mathematics• Mechanical engineering• Molecular biology• Nanocomputing• Neuroscience• Robotics• Social sciences• Statistical physics
… advances via Computational Thinking
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Range of Societal Issues Addressed
• Cancer therapy• Climate change• Environment• Visually impaired• Water
CISE
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Core and Cross-Cutting Programs
CNS IISCCF
Core Core
•Algorithmic F’ns•Communications & Information F’ns•Software & Hardware F’ns
• Human-Centered • Information Integra- tion & Informatics• Robust Intelligence
• Computer Systems• Network Systems
• Infrastructure• Education & Workforce
Core
Cross-Cutting• Cyber-Physical Systems• Data-intensive Computing• Network Science and Engineering• Trustworthy Computing
Plus many many other programs with other NSF directorates and other agencies
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Others
• Joint with other directorates and offices– CISE + BIO + SBE + MPS: Computational Neuroscience (with NIH)– CISE + EHR: Advanced Learning Technologies– CISE + ENG: Cyber-Physical Systems, Multi-core (with SRC)– CISE + MPS: FODAVA (with DHS), MCS– CISE + OCI: DataNet– OCI + CISE + ENG + GEO + MPS: PetaApps– Creative IT (co-funding with other directorates)
• Activities with other agencies, e.g., DARPA, DHS, IARPA, NGA, NIH, NSA• Partnerships with companies
– Google+IBM, HP+Intel+Yahoo!: Data-Intensive Computing– SRC: Multi-core
• Research infrastructure: CRI, MRI• …
Please see website www.cise.nsf.gov for full list.
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Trustworthy Computing
• Trustworthy = reliability, security, privacy, usability
• Deepen and broaden Cyber Trust
• Three emphases for FY09– Foundations of trustworthy
• Models, logics, algorithms, metrics– Privacy– Usability
Data Intensive Computing
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How Much Data?• NOAA has ~1 PB climate data (2007)• Wayback machine has ~2 PB (2006)• CERN’s LHC will generate 15 PB a year (2008)• HP is building WalMart a 4PB data warehouse (2007)• Google processes 20 PB a day (2008)• “all words ever spoken by human beings” ~ 5 EB• Int’l Data Corp predicts 1.8 ZB of digital data by 2011
640K ought to be enough for anybody.
Slide source: Jimmy Lin, UMD
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Convergence in Trends
• Drowning in data
• Data-driven approach in computer science research– graphics, animation, language translation, search, …, computational
biology
• Cheap storage– Seagate Barracuda 1TB hard drive for $90
• Growth in huge data centers
• Data is in the “cloud” not on your machine
• Easier access and programmability by anyone– e.g., Amazon EC2, Google+IBM cluster, Yahoo! Hadoop
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Data-Intensive ComputingSample Research Questions
Science– What are the fundamental capabilities and limitations of this paradigm? – What new programming abstractions (including models, languages,
algorithms) can accentuate these fundamental capabilities?– What are meaningful metrics of performance and QoS?
Technology– How can we automatically manage the hardware and software of these
systems at scale?– How can we provide security and privacy for simultaneous mutually
untrusted users, for both processing and data? – How can we reduce these systems’ power consumption?
Society– What (new) applications can best exploit this computing paradigm?
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Data-Intensive Computing Infrastructure for CISE Community
• Google + IBM partnership announced in February 2008– Access to 1600+ nodes, software and services (Hadoop, Tivoli, etc.)– Available to entire community– Cluster Exploratory (CluE) seed program– April 23, 2009: Press release on CluE awards to 14 universities
• http://www.nsf.gov/news/news_summ.jsp?cntn_id=114686&org=NSF&from=news
• HP + Intel + Yahoo! + UIUC cluster announced in July 2009– 1000+ nodes– Bare machine, not just software (Hadoop) accessible– Hosted at UIUC, available to entire community
• Other companies welcome!
Cyber-Physical Systems
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Smart Cars
Lampson’s Grand Challenge:
Reduce highway traffic deaths to zero.
[Butler Lampson, Getting Computers to Understand, Microsoft, J. ACM 50, 1 (Jan. 2003), pp 70-72.]
Cars drive themselvesCredit: PaulStamatiou.com
A BMW is “now actually a network of computers”
[R. Achatz, Seimens, Economist Oct 11, 2007]
Dash Express:Cars are nodes in a
network
Credit: Dash Navigation, Inc.
Smart parking
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Embedded Medical Devices
infusion pump
pacemaker
scanner
Credit: Baxter International
Credit: Siemens AG
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Sensors Everywhere
Sonoma Redwood Forest smart buildings
smart bridges
Credit: MO Dept. of Transportation
Credit: Arthur Sanderson at RPI
Hudson River Valley
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Robots Everywhere
At work: Two ASIMOs working together in coordination to deliver refreshments
Credit: Honda
At home: Paro, therapeutic robotic sealCredit: Paro Robots U.S., Inc.
At home/clinics: Nursebot, robotic assistance for the elderly
Credit: Carnegie Mellon University
At home: iRobot Roomba vacuums your house
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Assistive Technologies for Everyone
brain-computer interfaces of today
memex of tomorrow
Credit: Dobelle Institute
Credit: Emotiv
Credit: Paramount Pictures
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Cyber-Physical SystemsSample Research Challenges
Science• Co-existence of Booleans and Reals
– Discrete systems in a continuous world• Reasoning about uncertainty
– Human, Mother Nature, the Adversary
Technology• Intelligent and safe digital systems that interact with the
physical world• Self-monitoring, real-time learning and adapting
Society• Systems need to be unintrusive, friendly, dependable,
predictable, …
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A Model for Expediting Progress
FundamentalResearch
auto
finance
civil
aero
medical
chemical materials
energy
IndustryGov’t (e.g., military)
IndustryGov’tAcademia
AcademiaGov’t (NSF, NSA, NIH, DoD, DOE, …)
transportation
Sectors
Network Science and Engineering
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1999
Our Evolving Networks are Complex
19801970
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1999
Our Evolving Networks are Complex
19801970
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1999
Our Evolving Networks are Complex
19801970
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Network Science and Engineering
• Fundamental Question: Is there a science for understanding the complexity of our networks such that we can engineer them to have predictable behavior?
• Deepen and broaden research agenda of original GENI concept
• Includes CISE’s current networking programs: SING, FIND, NGNI
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Network Science and EngineeringSample Research Challenges
- Understand emergent behaviors, local–global interactions, system failures and/or degradations- Develop models that accurately predict and control network behaviors
- Develop architectures for self-evolving, robust, manageable future networks- Develop design principles for seamless mobility support- Leverage optical and wireless substrates for reliability and performance- Understand the fundamental potential and limitations of technology
- Design secure, survivable, persistent systems, especially when under attack- Understand technical, economic and legal design trade-offs, enable privacy protection- Explore AI-inspired and game-theoretic paradigms for resource and performance optimization
Science
Technology
SocietyEnable new applications and new economies, while ensuring security and privacy Security, privacy,
economics, AI, social science researchers
Network science and engineering researchers
Understand the complexity of large-scale networks
Distributed systems and substrate researchers
Develop new architectures, exploiting new substrates
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Network Science and EngineeringSample Research Challenges
- Understand emergent behaviors, local–global interactions, system failures and/or degradations- Develop models that accurately predict and control network behaviors
- Develop architectures for self-evolving, robust, manageable future networks- Develop design principles for seamless mobility support- Leverage optical and wireless substrates for reliability and performance- Understand the fundamental potential and limitations of technology
- Design secure, survivable, persistent systems, especially when under attack- Understand technical, economic and legal design trade-offs, enable privacy protection- Explore AI-inspired and game-theoretic paradigms for resource and performance optimization
Science
Technology
SocietyEnable new applications and new economies, while ensuring security and privacy Security, privacy,
economics, AI, social science researchers
Network science and engineering researchers
Understand the complexity of large-scale networks
Distributed systems and substrate researchers
Develop new architectures, exploiting new substrates
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NetSE Research Agenda:Background
• In January 2008, the CCC charged the NetSE Council Working Committee led by Ellen Zegura to develop a community-based process that would lead to a comprehensive agenda for the science and engineering of networks.
• Three workshops and two meetings, held between June and October 2008, elaborated the NetSE research space.
• A full draft of the agenda is now circulating among workshop co-chairs, CCC and NetSE members.
• The draft will be posted soon on the CCC website for comment.
July 29, 30, 2008: Science of Network Design, LA, CAOrganizers: John Doyle, Cal Tech; John Wroclawski, USC
July 29, 30, 2008: Science of Network Design, LA, CAOrganizers: John Doyle, Cal Tech; John Wroclawski, USC
July 31, Aug. 1, 2008: Behavior, Computation and Networks in Human Subject Experimentation, Del Mar, CAOrganizers: Michael Kearns, Penn; Colin Camerer, Cal Tech
July 31, Aug. 1, 2008: Behavior, Computation and Networks in Human Subject Experimentation, Del Mar, CAOrganizers: Michael Kearns, Penn; Colin Camerer, Cal Tech
August 17,18, 2008: Network Design and Engineering, Seattle, WAOrganizer: Ellen Zegura, GA Tech
August 17,18, 2008: Network Design and Engineering, Seattle, WAOrganizer: Ellen Zegura, GA Tech
September 24, 25, 2008: Network Design and Societal Values, Arlington, VA
Organizers: Helen Nissenbaum, NYU; Dave Clark, MIT
September 24, 25, 2008: Network Design and Societal Values, Arlington, VA
Organizers: Helen Nissenbaum, NYU; Dave Clark, MIT
Ellen Zegura developed an introduction that synthesizes the main themes across the workshops
Ellen Zegura developed an introduction that synthesizes the main themes across the workshops
June 11, 2008: Theory of Networked Computing, Boston, MA Organizer: Joan Feigenbaum, Yale
June 11, 2008: Theory of Networked Computing, Boston, MA Organizer: Joan Feigenbaum, Yale
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NetSE Research Agenda
GENIScience Plan
Why WeDream of GENI
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Network Research Infrastructure
• A parallel activity -- community planning for a suite of infrastructure that will support NetSE experiments -- has been underway since 2005.
• Requirements will evolve with the evolving NetSE research agenda.
• In 2007, CISE funded BBN to provide overall management of and engineering for infrastructure planning and prototyping.
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GENI Spiral 1• We are here
GENI Prototyping Plan
Multiple competing control frameworks,beginnings of an at-scale experiment plane
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Key goals for Spiral 1 Clusters
• Integrate “vertically”into the cluster control
framework
• Integrate “horizontally”to create end-to-end slices
• Demonstrate (early) integrated prototypesup and stumbling within the next 3-6 months
• . . . and design GENI as we go!
Components
Aggregate AComputer
Cluster
Components
Aggregate BBackbone Net
Components
Aggregate CMetro Wireless
ReferenceDesign
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Spiral 1 Academic-Industrial Teams
CNRI
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Current status - GENI Spiral 1Rapid prototyping, integration, and early experiments
Broadband
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Broadband• Mid-December
– CISE works with a handful of academics, led by Ed Lazowska (CCC) to provide a vision on Cyberinfrastructure for eScience and eLearning for President-Elect Transition Team.
– CISE independently works with staffers and OLPA on Cyberinfrastructure vision
• Mid-February: “Unleashing Waves of Innovation” white paper released publicly to community– http://www.cra.org/ccc/docs/init/Unleashing.pdf– Endorsed by many state and regional network consortia, National Lambda
Rail, Internet 2, etc.– Latest version on CISE AC website
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4. Colleges and universities serve as neutral territory for open, non-proprietary, unclassified work, fostering partnerships with industry and government.
1. Colleges and universities are innovation incubators.
5. Colleges and universities are community hubs.
Unleashing Waves of Innovation
CSNET NSFNET vBNS
2. College and university applicationsdrive innovation in networking.
3. Colleges and universities have a four-decade proven track record in deploying and managing advanced networking.
Credit: NASA
Credit: Wikipedia
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Broadband con’td
• Feb 17: NTIA + RUS get $7B in ARRA for start of national broadband strategy; NSF’s ARRA $200M ARI and $300M MRI are potential sources of funding for universities too. – Be on the lookout for calls for proposals for NTIA BTOP and NSF
ARI and MRI programs.• April 24: Secretary Locke (Commerce) and Dr. Bement
(NSF) meet– NTIA+RUS and NSF will continue to work closely on a national
broadband strategy for ARRA and beyond• April-summer: CISE and OCI will work with NTIA+RUS on
joint advisory committee, review process, etc.
Research Ideas in the Works
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ClickworkersCollaborative Filtering
Collaborative IntelligenceCollective Intelligence
CrowdsourcingHuman-Based Computation
Recommender SystemsReputation Systems
Social CommerceSwarm Intelligence
WikinomicsWisdom of the Crowds
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Socially Intelligent Computing
• Multiple dimensions– Numbers and types of people– Numbers and types of devices and services– Numbers and types of communications and interactions
• Examples– Individual Memexes, personalized robots, social networks,
Second Life++, human computation
• Question: Can we harness these capabilities to make humans and computers work effectively in harmony, solving problems neither can solve alone?
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Green IT
IT as part of the problem and IT as part of the solution
• IT as a consumer of energy– 2% (and growing) of world-wide energy use due to IT
• IT as a helper to solve problems– Direct: reduce energy use, recycle, repurpose, …– Indirect: e-commerce, e-collaboration, telework -> reduction travel, …– Systemic: computational models of climate, species, … -> inform science
and inform policy
• Broader context: Sustainability, Energy, Climate Change, Economy, Human Behavior
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Computer Science and Economics
- Automated mechanism design underlies electronic commerce, e.g., ad placement, on-line auctions, kidney exchange
- Internet marketplace requires revisiting Nash equilibria model
- Use intractability for voting schemes to circumvent impossibility results
- Emphasis on foundational aspects (e.g., algorithms, game theory) but clearly contributions/participation from AI (multi-agents) and systems communities (and hence overlaps a bit with Human-Computer Intelligence and NetSE)
Computer Science influencing EconomicsEconomics influencing Computer Science
Last Word:The Future of Computing is Bright!
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Drivers of Computing
Science
Society
Technology• What is computable?• P = NP?• (How) can we build complex systems simply?• What is intelligence?• What is information?
J. Wing, “Five Deep Questions in Computing,” CACM January 2008
7A’sAnytime Anywhere AffordableAccess to Anything by Anyone Authorized.
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Drivers of Computing
Science
Society
Technology• What is computable?• P = NP?• (How) can we build complex systems simply?• What is intelligence?• What is information?
J. Wing, “Five Deep Questions in Computing,” CACM January 2008
7A’sAnytime Anywhere AffordableAccess to Anything by Anyone Authorized.
Thank You!
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Credits
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