[hpc-announce] 6th Workshop on Scientific Cloud Computing (ScienceCloud) @HPDC 2015

Alexandru Costan alexandru.costan at inria.fr
Wed Jan 21 06:31:39 CST 2015

Call for Papers: 6th Workshop on Scientific Cloud Computing (ScienceCloud)
June 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 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


- Samer Al-Kiswany, University of Wisconson - Madison
- Roy Campbell,	 University of Illinois at Urbana-Champaign
- David Chiu, University of Puget Sound		
- 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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