[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

[HTCondor-users] CFP: Scientific Cloud Computing (ScienceCloud) -- co-located with ACM HPDC 2013

*** Call for Papers ***
4th Workshop on Scientific Cloud Computing (ScienceCloud) 2013
Co-located with ACM HPDC 2013, New York City, NY, USA -- June 17th, 2013

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 4th 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
- Virtualization
- Distributed Operating Systems
- Many-core computing and accelerators (e.g. GPUs, MIC) in the Cloud
- Cloud security

- Paper submission: February 11th, 2013 (11:59PM PST)
- Acceptance notification: March 18th, 2013
- Final papers due: April 15th, 2013

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/ScienceCloud2013/ before the deadline of
February 11th, 2013 at 11:59PM PST. 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 March 18th, 2013. Selected excellent work will be invited to submit extended versions of
the workshop paper to a special issue journal. Submission implies the
willingness of at least one of the authors to register and present the paper.

- Ioan Raicu, Illinois Institute of Technology & Argonne National Lab., USA
- Yogesh Simmhan, University of Southern California, USA

- Kyle Chard, University of Chicago, USA
- Gabriel Antoniu, INRIA, France
- Lavanya Ramakrishnan, Lawrence Berkeley National Lab, USA

- 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
- Kate Keahey, University of Chicago & Argonne National Laboratory, 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

- Samer Al-Kiswany (University of British Columbia)
- Roger Barga (Microsoft Research)
- Roy Campbell (University of Illinois at Urbana Champaign)
- Charlie Catlett (Argonne National Laboratory)
- Simon Caton (KIT)
- David Chiu (Washington State University)
- Jack Dongara (University of Tennessee)
- Ake Edlund (Royal Institute of Technology)
- Chathura Herath (Indiana University)
- Neil Chue Hong (University of Edinburgh)
- Adriana Iamnitchi (University of South Florida)
- Shantenu Jha (Louisiana State University)
- Hui Jin (Illinois Institute of Technology)
- Carl Kesselman (University of Southern California)
- Thilo Kielmann (Vrije University)
- Gregor von Laszewski (Indiana University)
- Shiyong Lu (Wayne State University)
- Wei Lu (Microsoft Research)
- Andr Luckow (Louisiana State University)
- David Martin (Argonne National Laboratory)
- Gabriel Mateescu (Virginia Tech)
- Paolo Missier (University of Manchester)
- Ruben Montero (Universidad Complutense de Madrid)
- Reagan Moore (University of North Carolina)
- Jose Moreira (IBM Research)
- Christine Morin (INRIA Rennes)
- 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)
- Vasudeva Varma (IIIT-Hyderabad)
- 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
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/