The Institutional Role in a Technology-Intensive Collaborative Research Enterprise

1. The Institutional Role in a Technology-Intensive Collaborative Research Enterprise Peter M. Siegel Chief Information Officer University of California, Davis AIChE Annual Meeting San Francisco, California, November 2006 [Based on a presentation by Prof. Bernd Hamann, Assoc. VC Research, UC Davis]

2. A CyberView of the UC Davis Campus Courtesy Bernd Hamann, Office of Research, UC Davis

3. Examples: Major Computing Efforts(UC Davis) • Genome sciences • Earth sciences and geophysics • Astrophysics / cosmology • Telemedicine • Air quality • Earthquake engineering • Fluid dynamics • Materials • Chemistry • Dynamical systems • Hazards Prediction, Analysis, Planning

4. Examples: Computer / Computational Science Research(UC Davis) • Center for Computational Science and Engineering • John Rundle et al. • Computer Security Laboratory • Karl Levitt et al., Computer Science) • Networks • Ben Yoo et al., Biswanath Mukherjee et al., Electrical and Computer Engineering and Computer Science • Numerical linear algebra • Zhaojun Bai et al., Math. and Computer Science) • Database & information systems • Michael Gertz, Bertram Ludaescher et al., Computer Science • Bioinformatics • Dan Gusfield, Patrice Koehl, Oliver Fiehn et al., Computer Science and Genome Center • UC Davis Institute for Data Analysis & Visualization • Ken Joy, Bernd Hamann et al. • KeckCAVES / virtual reality • Louise Kellogg et al., Geology et al.

5. Cyberinfrastructure Data Challenges • Massive amounts of data (experiments, imaging technology, sensors and sensor networks, numerical simulations, …) • Physical “bench” experimentalists needs often outpace those of informatics / computational experts • Interdisciplinary exploration for multiple purposes • Computational science, experimental science, and imaging: Interactive analysis, feature extraction, monitoring simulations, … • Environmental hazards: water, air, and land; civil infrastructure • Decision making: Crisis management • Standards / Metadata can often be in conflict across groups • Real-time interaction for different groups – Networks! • Scientists and engineers • Decision and policy makers • Emergency response teams

6. Collaborative Cyberinfrastructure • Networks connecting multiple, heterogeneous interactive visual computing environments • Collaborativeanalysis and design done in real time, by teams of scientists/engineers at different sites

7. Distributed Networked Cyberinfrastructure Interaction portals (from CAVES to cell phones – for steering, collaboration, decisions, …) Servers (simulation, analysis, image generation, …) Data generators (computers, sensors, telescopes, cameras, …)

8. A Testbed of Testbeds Visualization Human Centered Computing Smart Classroom Earthquake Engineering Smart Building Motorola Pagewriter 2000 WLAN / Bluetooth Environmental Monitoring Pager H.323 GW Wearable Displays CITRIS-Network Smart Dust Sensor Network Tiny OS Millennium Cluster Courtesy Prof. Ben Yoo, UC Davis Branch Director, CITRIS

9. Effective Cyberinfrastructure Requires Campus Leadership and Investment • Cyberinfrastructure requires agile, proactive institutional planning • Broad and dispersed collaborations critical to institutional competitiveness, not just for innovative faculty • Not practical for each research community to build an (inter-)national infrastructure on their own • Institutional role in developing and protecting cyberinfrastructure and preserving institutional data is complex… • But who else will do it?

10. Facts and ConsiderationsCyberinfrastructure on the institutional critical path - 1 of 4 • Many research efforts at UC Davis are critically dependent on innovative computing and a world-class cyberinfrastructure • These dependencies are expanding in complexity and cost

11. Facts and ConsiderationsCyberinfrastructure on the institutional critical path - 2 of 4 • Traditional “wet lab” researchers often have larger need for cyberinfrastructure and informatics expertise than computational researchers in the same field • Large-scale simulation / computational analysis, plus: • Large-scale data collection (real-time sensor data design and use) • Metadata generation • Large-scale data dissemination and archival • Research computing efforts are diverse and require “solutions” that consider the distinct needs of different groups (e.g., computer scientists vs. computational scientists working with clusters)

12. Facts and ConsiderationsCyberinfrastructure on the institutional critical path - 3 of 4 • Institutional planning for facilities and resources can be glacial in pace, • unintentionally providing disincentives to innovative faculty • Increased role of research computing capabilities in competitive awards is leading to pressure to develop better cyberinfrastructure support • Minor issues now are show stoppers: space, power, cooling can dwarf cost and complexity of equipment • Renovations for cooling and power often cost more than the clusters!

13. Facts and ConsiderationsCyberinfrastructure on the institutional critical path - 4 of 4 • Researchers require improved infrastructure support for research computing in areas including: • (distributed) cluster computing (access, storage, cooling) • data “warehousing” and data sharing infrastructure • archiving • Managing high-speed networking technology • data mining, analysis and exploration facilities • range of “standard” support models for clusters and other technologies as they commoditize • Collaborative toolkits

14. Facts and Considerations Broad and dispersed collaborations critical • Campus cyberinfrastructure plans must consider and “complement” facilities available at national labs and other shared facilities • Growing role of “translational research” and priority on effective communications with national and state stakeholders require institutions enable efforts with broad impact • Models of value to public policy • Increased role of visualization and simulations as general communications tools with public and community broadly • A picture is worth 103 words, a simulation 106

15. Facts and ConsiderationsNot practical for each research community to build an (inter-)national infrastructure on their own • Many areas require long-term co-planning and investment in networking, common services, including security • UC System and CENIC • CIC (Big 10) universities : Illinois, Michigan, etc. • Internet2 / NLR • Middleware • Digital Library initiatives • Data standards allowing/encouraging use across disciplines • Long-term metadata and archival strategy • Ease of use a major hurdle • Critical issues are often infrastructure • Chilled water / cooling, power, site planning, competition between clusters and faculty collaborative spaces

16. Conclusions

17. Institutions Must Plan Well! • Medium- and long-term planning must complement faculty planning • Flexible, anticipating obsolescence • Multi-institutional planning and cooperation essential

18. Institutions Must Be Systematic Perform campus-wide state-of-affairs and needs analysis • The faculty must be in control, but needs must be anticipated at campus level for large-scale and longer-term infrastructure • “Pre-competitive” collaborations among geographically and intellectually close campuses may provide competitive advantage and better science-engineering infrastructure • Shared risk, co-investment, complementary facilities • Distributes data and cyber-services to enhance disaster survivability • New models for collaboration are needed • Bricks and mortar still perceived as critical to program success, yet slow, expensive, and often inefficient • Interdisciplinary programs must be agile and of relatively short duration (10s of years)

19. Institutions MustInvest well Resources for campuses to make these investments are limited • Challenge is: research and state dollars do not come close to covering full costs of research infrastructure investments needed • Best approach is to be driven by faculty success • “Instrument” the faculty to understand how their research excellence translates into cyber-infrastructure and related needs • Corollary: Multi-institutional infrastructure planning is essential • Networking and grid infrastructure just the first step

20. Last word: Institutions Must Take Risks • Based on assessment • Faculty-driven • Building on traditional strengths • Building towards a new and different future