The Big Data Interagency Working Group (BD IWG) works to facilitate and further the goals of the White House Big Data R&D Initiative.
The CPS IWG is to coordinate programs, budgets, and policy recommendations for Cyber Physical Systems (CPS) research and development (R&D).
Cyber Security and Information Assurance (CSIA) Interagency Working Group coordinates the activities of the CSIA Program Component Area.
The Health Information Technology Research and Development Interagency Working Group coordinates programs, budgets and policy recommendations for Health IT R&D.
HCI&IM focuses on information interaction, integration, and management research to develop and measure the performance of new technologies.
HCSS R&D supports development of scientific foundations and enabling software and hardware technologies for the engineering, verification and validation, assurance, and certification of complex, networked, distributed computing systems and cyber-physical systems (CPS).
The HEC IWG coordinates the activities of the High End Computing (HEC) Infrastructure and Applications (I&A) and HEC Research and Development (R&D) Program Component Areas (PCAs).
LSN members coordinate Federal agency networking R&D in leading-edge networking technologies, services, and enhanced performance.
The purpose of the SPSQ IWG is to coordinate the R&D efforts across agencies that transform the frontiers of software science and engineering and to identify R&D areas in need of development that span the science and the technology of software creation and sustainment.
Formed to ensure and maximize successful coordination and collaboration across the Federal government in the important and growing area of video and image analytics
The Wireless Spectrum R&D (WSRD) Interagency Working Group (IWG) has been formed to coordinate spectrum-related research and development activities across the Federal government.
Toward automated problem analysis of large-scale storage
• End goal: self-healing (autonomics; Self-* Storage)
• automatically detect, diagnose, and repair problems
• or, better, replace “detect” with “predict” and be proactive
• Least understood aspect: the “diagnose” step
• need deep instrumentation
• automatically identify failed components (to repair)
• automatically identify root causes (to prevent recurrences)
• Some key sub-questions
• trade-off between instrumentation detail and accuracy
• pros/cons of different algorithms for different problems
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