Call for Papers
IEEE Transactions on Cloud Computing
Special Issue on Scientific Cloud Computing
http://datasys.cs.iit.edu/
------------------------------
IMPORTANT DATES
Paper Submissions Due: July 31, 2014
First Round Decision: September 30,2014
Major Revisions Due (if neccesary): October 31, 2014
Final Decision: December 1, 2014
Journal Publication: TBD
------------------------------
OVERVIEW
Computational and Data-Driven Sciences have become the third and fourth pillar
of scientific discovery in addition to experimental and theoretical sciences.
Scientific Computing has already begun to change how science is done, enabling
scientific breakthroughs through new kinds of experiments that would have been
impossible only a decade ago. It is the key to solving ?grand challenges? in
many domains and providing breakthroughs in new knowledge, and it comes in many
shapes and forms: high-performance computing (HPC) which is heavily focused on
compute-intensive applications; high-throughput computing (HTC) which focuses
on using many computing resources over long periods of time to accomplish its
computational tasks; many-task computing (MTC) which aims to bridge the gap
between HPC and HTC by focusing on using many resources over short periods of
time; and data-intensive computing which is heavily focused on data
distribution, data-parallel execution, and harnessing data locality by
scheduling of computations close to the data. Today?s ?Big Data? trend is
generating datasets that are increasing exponentially in both complexity and
volume, making their analysis, archival, and sharing one of the grand
challenges of the 21st century. Not surprisingly, it becomes increasingly
difficult to design and operate large scale systems capable of addressing these
grand challenges.
This journal Special Issue on Scientific Cloud Computing in the IEEE
Transaction on Cloud Computing will provide the scientific community a
dedicated forum for discussing new research, development, and deployment
efforts in running these kinds of scientific computing workloads on Cloud
Computing infrastructures. This special issue will focus on the use of
cloud-based technologies to meet new compute-intensive and data-intensive
scientific challenges that are not well served by the current supercomputers,
grids and HPC clusters. The special issue will aim to address questions such
as: What architectural changes to the current cloud frameworks (hardware,
operating systems, networking and/or programming models) are needed to support
science? Dynamic information derived from remote instruments and coupled
simulation, and sensor ensembles that stream data for real-time analysis are
important emerging techniques in scientific and cyber-physical engineering
systems. How can cloud technologies enable and adapt to these new scientific
approaches dealing with dynamism? How are scientists using clouds? Are there
scientific HPC/HTC/MTC workloads that are suitable candidates to take advantage
of emerging cloud computing resources with high efficiency? Commercial public
clouds provide easy access to cloud infrastructure for scientists. What are the
gaps in commercial cloud offerings and how can they be adapted for running
existing and novel eScience applications? What benefits exist by adopting the
cloud model, over clusters, grids, or supercomputers? What factors are limiting
clouds use or would make them more usable/efficient?
------------------------------
TOPICS
The topics of interest are, but not limited to, the application of Cloud in
scientific applications:
? Scientific application cases studies on Clouds
? Performance evaluation of Cloud technologies
? Fault tolerance and reliability in cloud systems
? Data-intensive workloads and tools on Clouds
? Programming models such as Map-Reduce
? Storage cloud architectures
? I/O and Data management in the Cloud
? Workflow and resource management in the Cloud
? NoSQL databases for scientific applications
? Data streaming and dynamic applications on Clouds
? Dynamic resource provisioning
? Many-Task Computing in the Cloud
? Application of cloud concepts in HPC environments
? Virtualized High performance parallel file systems
? Virtualized high performance I/O networks
? Virtualization and its Impact on Applications
? Distributed Operating Systems
? Many-core computing and accelerators in the Cloud
? Cloud security
------------------------------
SUBMISSION INSTRUCTIONS
Authors are invited to submit papers with unpublished, original work to the
IEEE Transactions on Cloud Computing, Special Issue on Scientific Cloud
Computing. If the paper is extended from a workshop or conference paper, it
must contain at least 50% new material with "brand" new ideas and results. The
papers should not be longer than 14 double column pages in the IEEE TCC format.
Papers should be submitted directly to TCC at
https://mc.manuscriptcentral.
be
selected.
------------------------------
ORGANIZERS
? Kate Keahey, University of Chicago & Argonne National Laboratory, USA
? Ioan Raicu, Illinois Institute of Technology & Argonne National Lab., USA
? Kyle Chard, University of Chicago & Argonne National Laboratory, USA
? Bogdan Nicolae, IBM Research, Ireland
------------------------------
CONTACT
Email: sciencecloud2014-tcc-editors@
Website: http://datasys.cs.iit.edu/
----------------------
Bogdan Nicolae Tel: +353 (0)1 - 826 9253
Exascale Systems E-mail: bogdan.nicolae@ie.ibm.com
IBM Research, Ireland
Server 3, Damastown Industrial Park, Mulhuddart, Dublin 15, Ireland
Web: http://researcher.ibm.com/
IEEE Transactions on Cloud Computing
Special Issue on Scientific Cloud Computing
http://datasys.cs.iit.edu/
------------------------------
IMPORTANT DATES
Paper Submissions Due: July 31, 2014
First Round Decision: September 30,2014
Major Revisions Due (if neccesary): October 31, 2014
Final Decision: December 1, 2014
Journal Publication: TBD
------------------------------
OVERVIEW
Computational and Data-Driven Sciences have become the third and fourth pillar
of scientific discovery in addition to experimental and theoretical sciences.
Scientific Computing has already begun to change how science is done, enabling
scientific breakthroughs through new kinds of experiments that would have been
impossible only a decade ago. It is the key to solving ?grand challenges? in
many domains and providing breakthroughs in new knowledge, and it comes in many
shapes and forms: high-performance computing (HPC) which is heavily focused on
compute-intensive applications; high-throughput computing (HTC) which focuses
on using many computing resources over long periods of time to accomplish its
computational tasks; many-task computing (MTC) which aims to bridge the gap
between HPC and HTC by focusing on using many resources over short periods of
time; and data-intensive computing which is heavily focused on data
distribution, data-parallel execution, and harnessing data locality by
scheduling of computations close to the data. Today?s ?Big Data? trend is
generating datasets that are increasing exponentially in both complexity and
volume, making their analysis, archival, and sharing one of the grand
challenges of the 21st century. Not surprisingly, it becomes increasingly
difficult to design and operate large scale systems capable of addressing these
grand challenges.
This journal Special Issue on Scientific Cloud Computing in the IEEE
Transaction on Cloud Computing will provide the scientific community a
dedicated forum for discussing new research, development, and deployment
efforts in running these kinds of scientific computing workloads on Cloud
Computing infrastructures. This special issue will focus on the use of
cloud-based technologies to meet new compute-intensive and data-intensive
scientific challenges that are not well served by the current supercomputers,
grids and HPC clusters. The special issue will aim to address questions such
as: What architectural changes to the current cloud frameworks (hardware,
operating systems, networking and/or programming models) are needed to support
science? Dynamic information derived from remote instruments and coupled
simulation, and sensor ensembles that stream data for real-time analysis are
important emerging techniques in scientific and cyber-physical engineering
systems. How can cloud technologies enable and adapt to these new scientific
approaches dealing with dynamism? How are scientists using clouds? Are there
scientific HPC/HTC/MTC workloads that are suitable candidates to take advantage
of emerging cloud computing resources with high efficiency? Commercial public
clouds provide easy access to cloud infrastructure for scientists. What are the
gaps in commercial cloud offerings and how can they be adapted for running
existing and novel eScience applications? What benefits exist by adopting the
cloud model, over clusters, grids, or supercomputers? What factors are limiting
clouds use or would make them more usable/efficient?
------------------------------
TOPICS
The topics of interest are, but not limited to, the application of Cloud in
scientific applications:
? Scientific application cases studies on Clouds
? Performance evaluation of Cloud technologies
? Fault tolerance and reliability in cloud systems
? Data-intensive workloads and tools on Clouds
? Programming models such as Map-Reduce
? Storage cloud architectures
? I/O and Data management in the Cloud
? Workflow and resource management in the Cloud
? NoSQL databases for scientific applications
? Data streaming and dynamic applications on Clouds
? Dynamic resource provisioning
? Many-Task Computing in the Cloud
? Application of cloud concepts in HPC environments
? Virtualized High performance parallel file systems
? Virtualized high performance I/O networks
? Virtualization and its Impact on Applications
? Distributed Operating Systems
? Many-core computing and accelerators in the Cloud
? Cloud security
------------------------------
SUBMISSION INSTRUCTIONS
Authors are invited to submit papers with unpublished, original work to the
IEEE Transactions on Cloud Computing, Special Issue on Scientific Cloud
Computing. If the paper is extended from a workshop or conference paper, it
must contain at least 50% new material with "brand" new ideas and results. The
papers should not be longer than 14 double column pages in the IEEE TCC format.
Papers should be submitted directly to TCC at
https://mc.manuscriptcentral.
be
selected.
------------------------------
ORGANIZERS
? Kate Keahey, University of Chicago & Argonne National Laboratory, USA
? Ioan Raicu, Illinois Institute of Technology & Argonne National Lab., USA
? Kyle Chard, University of Chicago & Argonne National Laboratory, USA
? Bogdan Nicolae, IBM Research, Ireland
------------------------------
CONTACT
Email: sciencecloud2014-tcc-editors@
Website: http://datasys.cs.iit.edu/
----------------------
Bogdan Nicolae Tel: +353 (0)1 - 826 9253
Exascale Systems E-mail: bogdan.nicolae@ie.ibm.com
IBM Research, Ireland
Server 3, Damastown Industrial Park, Mulhuddart, Dublin 15, Ireland
Web: http://researcher.ibm.com/
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