[CAnet - news] NSF DDDAS Solicitation and CANARIE i-Infrastructure programs

For more information on this item please visit the CANARIE CA*net 4 Optical
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[Those interested in applying to CANARIE's recently announced
i-Infrastructure Program (CIIP) should also look at the recently announced
NSF-NIH-NOAA Dynamic Data Driven Applications Systems (DDAS) solicitation.
Both programs have similar objectives, although the CANARIE program is more
specific in the recommended use of Service Oriented Architectures and web
services workflow to enable "symbiotic feedback control systems".  As the
NSF solicitation describes, it is believed there is wide application of
these technologies in a variety of fields including networking, process
control, biological and behavioral sciences, manufacturing, etc. A good
example of these concepts is the recently announced UK e-Science Geodise
program which use service oriented architectures with web services, workflow
tools and grids for engineering design and development in applications such
as computational fluid dynamics, MatLab applications, etc.  Note that
international collaborations are encouraged in all 3 programs.
-- BSA] 

For information on CANARIE's i-Infrastructure program please see
http://www.canarie.ca/funding/ciip/guidelines.html

For more information on the UK e-Science program Geodise please see
http://www.geodise.org

Excerpts from the NSF DDDAS solicitation:
www.cise.nsf.gov/dddas



I. INTRODUCTION

Information technology-enabled applications/simulations of systems in
science and engineering have become as essential to advances in these fields
as theory and measurement. 
[..]
This solicitation focuses explicitly on Dynamic Data Driven Applications
Systems (DDDAS), a promising concept in which the computational and
experimental measurement aspects of an application are dynamically
integrated, creating new capabilities in many application areas of science
and engineering. Computational aspects of DDDAS may be realized on a diverse
set of computer platforms including computational grids encompassing
leadership-class supercomputers, mid-range clusters, distributed,
high-throughput computing environments, and sensor networks. Enabling DDDAS
requires multidisciplinary research that focused on generating advances in
applications, application algorithms, systems software, and measurement
approaches. As such, DDDAS-funded projects are expected to make significant
contributions to research advances in computational science, high-end
computing and cyberinfrastructure.

DDDAS is a paradigm where application/simulations and measurements become a
symbiotic feedback control system. DDDAS entails the ability to dynamically
incorporate additional data into an executing application, and in reverse,
the ability of an application to dynamically steer the measurement process. 
[..]
The need for such dynamic applications is already emerging in business,
engineering and scientific processes, analysis, and design. Manufacturing
process controls, resource management, weather and climate prediction,
traffic management, systems engineering, civil engineering, geo-exploration,
social and behavioral modeling, cognitive measurement and bio-sensing are
examples of areas likely to benefit from the DDDAS paradigm. DDDAS has the
potential to transform the way science and engineering is done, and impact
many aspects of our society, including manufacturing, commerce,
transportation, hazard prediction/management, ecology and environmental
biology, medicine, security and emergency response, to name a few.
[..]
With the DDDAS concept the measurement system becomes part of the "platform"
supporting the application. Thus, DDDAS requirements extend the current
notion of "grid" infrastructure to include measurement systems in a
dynamically integrated way.

.
II. PROGRAM DESCRIPTION

Research and Education Themes

[..]
Below are listed some of the technical challenges covered in the four DDDAS
research component areas; this list is illustrative rather than exhaustive.
In addition, a list of illustrative examples of applications and application
areas is provided at the end of this section and in the "examples of
applications" web link on the DDDAS webpage (www.cise.nsf.gov/dddas).

[..]
            Application Composition: dynamic selection of models, based on
data dynamically streamed into the executing application; application model
interfaces and application knowledge based systems to create the ability to
invoke such multiple scales at runtime; stochastic application models.
          o

            Application-Data Interfaces: application-measurement interfaces
and data models (dynamic data streaming into the application / controlling
measurement processes); application-measurement time-scale correlation;
asynchronous data collection; dynamic data-assimilation; continuous data
streams in addition to discrete data sets.

    *
[...]
    *

      Systems Software Infrastructure: Advances are also necessary in
systems software supporting the execution of applications whose systems
requirements are dynamically dependent on dynamic data inputs. In addition,
new systems software approaches are required that support integrated
computational and measurement system software architecture, interfaces and
best practices of applications software with measurement systems, including
sensor systems, and systems software to manage underlying computational grid
resources and measurement systems.
          o

            Dynamic Application Execution Support Environments: Support for
dynamic selection at runtime of Application Components. Multi-resolution
capabilities (that is scaling for multiple levels of resolution) are
essential in DDDAS systems. That will require systems software to
dynamically select application components, embodying algorithms suitable for
the kinds of solution approaches depending on the streamed data, and
depending on the underlying resources.
          o

            Dynamic Computing Requirements and Matching Dynamic Resource
Requirements: DDDAS will employ heterogeneous platform environments such as
high end and grid computational platforms, embedded sensors for
data-collection, distributed high-performance simulations environments, and
special-purpose platforms for pre- and post processing of data, for example
dynamic data assimilation and visualization. Such environments require
systems software supporting dynamic discovery of computational resources to
match the changing requirements of the applications, dynamic and adaptive
application execution, resource management taking into account the unified
computational and instruments platforms, and supporting the runtime of such
systems with fault tolerance and Quality of Service (QoS).
          o Interfaces to Physical Devices (including sensor systems,
detectors for spectrometers of all kinds, such as synchrotron and neutrons
sources) and Dynamic Data Management Requirements: Software supporting
Application/Measurement Interfaces together with new capabilities for
managing measurement systems, sensors and actuators is required, which are
additional "resources" in the computational grid. Accordingly, resource
discovery and allocation of sensors, and ways to architect the set of
sensors to behave as a "system" become important issues. Computer and
network modeling and simulation methods that extend beyond current
approaches by dynamically incorporating on-line and archived measurements
from real networks are of interest. These will provide a better
understanding and more accurate prediction of network behavior for a wide
range of time-scales, a broad spectrum of spatial network topology
structures, and multiple protocols interacting with one another and across
the different networking layers, especially as one goes from wired to
wireless and streaming data from networked sensor systems. Integrating
application and measurement data also requires new approaches for data
management systems supporting different naming schemas or ontologies,
information services, or information views. Feedback systems must return
data in a timely fashion and useable form to computational resources and
instruments, as well as to individuals who might act on it.

[...]
DDDAS can have an impact in the biological sciences, engineering,
geosciences, materials, physical sciences, space and social sciences, and in
enabling infrastructure technologies. The following is an illustrative but
non-exhaustive list of possible applications/areas that may benefit from the
DDDAS paradigm (additional examples are given in the "examples of
applications" web link on the DDDAS webpage: www.cise.nsf.gov/dddas):

In engineering and physical systems, applications involving real-time and
virtual operations re-planning, process control and optimization situations
such as those occurring in manufacturing and service systems; in earthquake
tolerant structures, in crisis management, like fire propagation prediction
and containment; in complex structural and fluidic processes encountered in
chemical, mechanical and biomechanical systems; in new methodologies, like
in computing and measurements infrastructure systems, such as the design and
configuration methodologies for sensor networks and traditional networked
computer systems, in enhancing methods for improved engineering and for
enabling analysis and prediction in decision support systems ranging from
advanced driving assistance systems for automobiles to systems proposed for
air-traffic management, analysis on structural integrity of mechanical
systems, like analysis of systems of sensors, such predicting crack
development in aircraft fuselage, to neurobiological and bio-molecular
systems, to enhancing oil exploration methods, to improved analysis of
environmental systems observations; in chemical imaging; X-ray and neutron
scattering and spectroscopy.

In biological sciences, DDDAS can increase the accuracy of image-guided
interventions, improve the performance and utility of multi-scale models,
enhance methods to analyze and gain insights of the biodiversity and
bio-complexity of the world's terrestrial and aquatic communities and
ecosystems; complex models are needed integrating data from several scales
of observation, including real time measurements from remote sensing and
geographic information systems.

In geosciences, DDDAS methods can be used to address the non-linearities and
diverse spatial and time scales in the highly interactive system for example
of the hydro-complexity of weather, water and pollution processes

In the social and behavioral sciences, DDDAS can enable real time adaptive
approaches to interviewing, cognitive measurement and experimentation. It
can enable ways to better understand and to increase the efficiency of
business production, customer service, policing, crowd control, crisis
decision making, learning processes, language comprehension, and perception.

Recent advances in grid computing and sensor systems, high-end computing, as
well as cyberinfrastructure projects such as the Network for Earthquake
Engineering Simulation (NEES), the international Virtual Data Grid
Laboratory (iVDGL), the Large Hadron Collider (LHC), Chemistry and Materials
Consortium for Advanced Radiation Sources (ChemMatCARS), Energy Recovery
Linac (ERL), Vibrational Spectrometer for SNS (VISION) , Data Acquisition
For Neutron Scattering Experiments (DANSE), and Center for High Resolution
Neutron Scattering (CHRNS), the National Ecological Observatory Network
(NEON), and the Geosciences Network (GEON), are all excellent examples of
applications where DDDAS can be used to enhance research productivity and
the impact of simulation and measurements enabled by these infrastructure
projects.

[..]
DDDAS provides tremendous new opportunities of interest to industry. For
DDDAS, NSF and the other sponsor agencies welcome proposals for
collaborative projects involving both universities and the private
commercial sector, where:

    * US commercial organizations, especially small businesses with strong
capabilities in scientific or engineering research or education, participate
as subawardees. Proposals like these should be submitted by the due date
described in this solicitation; or
    * Small businesses submit projects or lead collaborative projects
involving academic partner organizations. The participation of NSF's STTR
and SBIR programs in this solicitation provides an alternate venue for
funding of such activities. Small business-led projects are encouraged to
apply to the NSF Small Business Innovation Research and Small Business
Technology Transfer Programs, SBIR and STTR ( http://www.nsf.gov/eng/sbir),
referencing the relation of their project to DDDAS. Investigators should
contact officials of the SBIR/STTR programs to discuss specifics.

[..]
International collaborations are also encouraged. Given the worldwide
expansion of research and education, international collaborations that
advance DDDAS goals and strengthen proposed project activities are
encouraged. There is opportunity for coordinated funding with colleagues
from foreign institutions who will add value to the project. The DDDAS
program will support US-based scientists. Collaborators in institutions
outside the US must seek funding from their respective funding
organizations. NSF requires that proposals with international collaborations
include the foreign collaborators' biographical sketches (CVs) and
documentation of their agreement to collaborate in the proposed project, as
well as the means by which they will support their part of the work.
International funding organizations that are co-operating in this
solicitation include: the EU e-Infrastructure (www.cordis.lu/ist/rn), Dr.
Kyriakos Baxevanidis, [email protected], the EU Information
Society Technologies (IST) Programme (www.cordis.lu/ist) and the Grid
Research under the IST Programme (www.cordis.lu/ist/grids), Dr. Max Lemke
([email protected]), and the UK e-Sciences Program, specifically the RCUK
e-Science Program, (www.rcuk.ac.uk/escience, and
www.rcuk.ac.uk/escience/links), Dr. James Flemming,
[email protected].



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These news items and comments are mine alone and do not necessarily reflect
those  of the CANARIE board or management.

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