ScienceCloud 2014 - 5th Workshop on Scientific Cloud Computing

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.
Topics
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
Important Dates
Paper submission: March 1st, 2014
Acceptance notification: April 4th, 2014
Final papers due: April 11th, 2014
Workshop date: June 23th/24th, 2014
Paper Submission
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-t...). A 250 word abstract and the final paper in PDF format must be submitted online at https://cmt.research.microsoft.com/SCIENCECLOUD201.... 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, 2014. 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. Submission implies the willingness of at least one of the authors to register and present the paper.
Organization
General Chairs
Ioan Raicu, Illinois Institute of Technology & Argonne National Laboratory, USA
Kate Keahey, University of Chicago & Argonne National Laboratory, USA
Program Committee Chairs
Kyle Chard, University of Chicago, USA
Bogdan Nicolae, IBM Research, Ireland
Steering Committee
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
Program Committee
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