[hpc-announce] Extended Deadline: 7th Workshop on Scientific Cloud Computing (ScienceCloud) colocated with HPDC'16, Kyoto, Japan

Alexandru Costan alexandru.costan at inria.fr
Tue Feb 23 08:37:43 CST 2016

Call for Papers: 7th Workshop on Scientific Cloud Computing (ScienceCloud)
Co-Located with HPDC 2016
Wed June 1st, 2016. Kyoto, Japan.
Web: http://www.irisa.fr/kerdata/sciencecloud/2016/ <http://www.irisa.fr/kerdata/sciencecloud/2016/>


Paper Submission Due: February 29, 2016
Notification of Acceptance: March 12, 2016
Camera Ready Version Due: March 27, 2016
Workshop: June 1st, 2016


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

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 <http://www.acm.org/sigs/publications/proceedings-templates>). Papers
will be peer-reviewed, and accepted papers will be published in the
workshop proceedings as part of the ACM digital library

Papers conforming to the above guidelines can be submitted through the
workshop's paper submission system:
https://easychair.org/conferences/?conf=sciencecloud2016 <https://easychair.org/conferences/?conf=sciencecloud2016>.


Kyle Chard, University of Chicago & Argonne National Laboratory, USA
Bogdan Nicolae, IBM Research, Ireland
Alexandru Costan, Inria/IRISA, France
Dongfang Zhao, Pacific Northwest National Laboratory, USA


Ian Foster, University of Chicago & Argonne National Lab, 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, USA
Jack Dongarra, University of Tennessee, USA
Geoffrey Fox, Indiana University, USA
Lavanya Ramakrishnan, Lawrence Berkeley National Laboratory, USA
Yogesh Simmhan, Indian Institute of Science, Bangalore, India
Gabriel Antoniu, INRIA, France
Ioan Raicu, Illinois Institute of Technology, USA


Samer Al-Kiswany, University of Wisconsin - Madison
Roy Campbell, University of Illinois at Urbana-Champaign
David Chiu, University of Puget Sound
Devarshi Ghoshal, Lawrence Berkeley National Laboratory
Chathura Herath, Indiana University
Marty Humphrey, University of Virginia
Dan Katz, University of Chicago
Thilo Kielmann, Vrije Universiteit
Gregor Laszewski, Indiana University
Shiyong Lu Wayne State University
David Martin, Argonne National Laboratory
Ruben Santiago Montero, Universidad Complutense de Madrid
Josh Simons, VMWare
Christine Morin, INRIA
Pasquale Pagano, CNR-ISTI
Beth Plale, Indiana University
Ioan Raicu, Illinois Institute of Technology
Omer Rana, Cardiff University
Matei Ripeanu, The University of British Columbia
Douglas Thain, University of Notre Dame
Johan Tordsson, Umea University
Vasudeva Varma, IIIT Hyderabad
Yong Zhao, University of Electronic Science and Technology of China

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