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ScienceCloud 2015 : Workshop on Scientific Cloud Computing


When Jun 15, 2015 - Jun 16, 2015
Where Portland, OR
Submission Deadline Feb 13, 2015
Notification Due Mar 20, 2015
Final Version Due Apr 1, 2015

Call For Papers

Call for Papers: 6th Workshop on Scientific Cloud Computing (ScienceCloud)
June 15/16, 2015. Portland, OR, USA

Co-Located with HPDC 2015


Paper Submission: February 13, 2015
Acceptance Notification: March 20, 2015
Final Papers: April 1, 2015
Workshop: June 15/16, 2015


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. Today.s .Big Data. science 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. The support for data intensive computing is critical to advance
modern science as storage systems have exposed a widening gap between their
capacity and their bandwidth by more than 10-fold over the last decade. There
is a growing need for advanced techniques to manipulate, visualize and
interpret large datasets. Scientific Computing 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.

The 6th workshop on Scientific Cloud Computing (ScienceCloud) 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. The ScienceCloud
workshop 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 workshop 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?

This workshop encourages interaction and cross-pollination between those
developing applications, algorithms, software, hardware and networking,
emphasizing scientific computing for such cloud platforms. We believe the
workshop will be an excellent place to help the community define the current
state, determine future goals, and define architectures and services for
future science clouds.


We invite the submission of original work that is related to the topics below.
The papers can be either short (4 pages) position papers, or long (8 pages)
research papers.

Topics of interest include (in the context of Cloud Computing):

Scientific application cases studies on Cloud infrastructure
Performance evaluation of Cloud environments and technologies
Fault tolerance and reliability in cloud systems
Data-intensive workloads and tools on Clouds
Use of programming models such as Map-Reduce and its implementations
Storage cloud architectures
I/O and Data management in the Cloud
Workflow and resource management in the Cloud
Use of cloud technologies (e.g., 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 or vice versa
High performance parallel file systems in virtual environments
Virtualized high performance I/O network interconnects
Distributed Operating Systems
Many-core computing and accelerators (e.g. GPUs, MIC) in the Cloud
Cloud security


Authors are invited to submit papers with unpublished, original work of not
more than 8 pages of double column text using single spaced 10 point size on
8.5 x 11 inch pages (including all text, figures, and references), as per ACM
8.5 x 11 manuscript guidelines (document templates can be found at
Submission implies the willingness of at least one of the authors
to register and present the paper.

Papers conforming to the above guidelines can be submitted through the
workshop's paper submission system:


- Kyle Chard, University of Chicago & ARgonne National Laboratory, USA
- Alexandru Costan, Inria/IRISA, France
- Bogdan Nicolae, IBM Research, Ireland
- Lavanya Ramakrishnan, Lawrence Berkeley National Laboratory, USA


- Gabriel Antoniu, INRIA, France
- Pete Beckman, University of Chicago & Argonne National Laboratory, USA
- Jack Dongarra, University of Tennessee, USA
- Ian Foster, University of Chicago & Argonne National Laboratory, USA
- Geoffrey Fox, Indiana University, USA
- Dennis Gannon, Microsoft Research, USA
- Carole Goble, University of Manchester, UK
- Robert Grossman, University of Chicago, USA
- Ed Lazowska, University of Washington & Computing Community Consortium, USA
- David O'Hallaron, Carnegie Mellon University, USA
- Ioan Raicu, Illinois Institute of Technology, USA
- Yogesh Simmhan, University of Southern California, USA

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