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[HTCondor-users] CFP: 5th Workshop on Scientific Cloud Computing (ScienceCloud) @ ACM HPDC 2014

Call for Papers:  5th Workshop on Scientific Cloud Computing (ScienceCloud)
June 23/24, 2014. Vancouver, Canada

Co-Located with HPDC 2014


Paper Submission: March 1, 2014
Acceptance Notification: April 4, 2014
Final Papers: April 11, 2014
Workshop: June 23/24, 2014


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 5th 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
http://www.acm.org/sigs/publications/proceedings-templates). A 250 word
abstract and the final paper in PDF format must be submitted online at
https://cmt.research.microsoft.com/SCIENCECLOUD2014/ before the deadline.
Papers will be peer-reviewed, and accepted papers will be published in the
workshop proceedings as part of the ACM digital library. Notifications of the
paper decisions will be sent out by April 4th, 2014. Submission implies the
willingness of at least one of the authors to register and present the paper.

JOURNAL SPECIAL ISSUE: IEEE Transaction on Cloud Computing

Selected excellent work will be invited to submit extended versions of the
workshop paper to the Special Issue on Scientific Cloud Computing in the
IEEE Transactions on Cloud Computing


- Ioan Raicu, Illinois Institute of Technology & Argonne National Laboratory, USA
- Kate Keahey, University of Chicago & Argonne National Laboratory, USA


- Kyle Chard, University of Chicago, USA
- Bogdan Nicolae, IBM Research, Ireland


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


- Samer Al-Kiswany, University of British Columbia
- Roger Barga, Microsoft Research
- Simon Caton, Karlsruhe Institute of Technology
- Ake Edlund, Royal Institute of Technology
- Chathura Herath, Indiana University
- Neil Chue Hong, University of Edinburgh
- Shantenu Jha, Rutgers
- Carl Kesselman, University of Southern California
- Thilo Kielmann, Vrije University
- Shiyong Lu, Wayne State University
- Wei Lu, Microsoft Research
- David Martin, Argonne National Laboratory
- Gabriel Mateescu, EURAC Research, Italy
- Paolo Missier, University of Manchester
- Ruben Montero, Universidad Complutense de Madrid
- Reagan Moore, University of North Carolina
- Pasquale Pagano, ISTI
- Beth Plale, Indiana University
- Omer Rana, Cardiff University
- Matei Ripeanu, University of British Columbia
- Josh Simons, VMWare
- Douglas Thain, University of Notre Dame
- Johan Tordsson, Ume University
- Zhifeng Yun, Louisiana State University
- Yong Zhao, University of Electronic and Science Technology of China

Ioan Raicu, Ph.D.
Assistant Professor, Illinois Institute of Technology (IIT)
Guest Research Faculty, Argonne National Laboratory (ANL)
Data-Intensive Distributed Systems Laboratory, CS/IIT
Distributed Systems Laboratory, MCS/ANL
Editor: IEEE TCC, Springer Cluster, Springer JoCCASA
Chair:  IEEE/ACM MTAGS, ACM ScienceCloud
Cel:      1-847-722-0876
Office:   1-312-567-5704
Email:    iraicu@xxxxxxxxxx
Web:      http://www.cs.iit.edu/~iraicu/
Web:      http://datasys.cs.iit.edu/
LinkedIn: http://www.linkedin.com/in/ioanraicu
Google:   http://scholar.google.com/citations?user=jE73HYAAAAAJ