CFP (PDF, TXT) | News | Topics | Dates | Submission | Organization | Program
5th Workshop on Scientific Cloud Computing (ScienceCloud) 2014
Co-located with ACM HPDC 2014Vancouver, Canada -- June 23rd, 2014
News
- Best paper award: Evaluating Storage Systems for Scientific Data in the Cloud
- Best paper nominations:
- Keynote "Software Defined Federated Infrastructures for Science" by Prof. Manish Parashar
- Program posted, starts at 8:45AM and runs to 4:45PM
- Special Issue on Scientific Cloud Computing in the IEEE Transactions on Cloud Computing
- Our review process has conlcuded with 8 accepted papers from 16
full submissions:
- "Science in the Cloud: Lessons from Three Years of Research Projects on Windows Azure", Dennis Gannon, Dan Fay, Daron Green, Wenming Ye, Kenji Takeda
- "Cloud Computing Data Capsules for Non-Consumptive Use of Texts", Jiaan Zeng, Guangchen Ruan, Alexander Crowell, Atul Prakash, Beth Plale
- "A Cloud Computing Approach to On-Demand and Scalable CyberGIS Analytics", Pierre Riteau, Myunghwa Hwang, Anand Padmanabhan, Yizhao Gao, Yan Liu, Kate Keahey, Shaowen Wang
- "Mux-Kmeans: Multiplex Kmeans for Clustering Large-scale Data Set", Chen Li, Yanfeng Zhang, Minghai Jiao, Ge Yu
- "Evaluating Storage Systems for Scientific Data in the Cloud", Ketan Maheshwari, Justin Wozniak, Hao Yang, Daniel S. Katz, Matei Ripeanu, Victor Zavala, Michael Wilde
- "HEP Computing in a Context-Aware Cloud Environment", Frank Berghaus, Andre Charbonneau, Ron Desmarais, Ian Gable, Colin Leavett-Brown, Michael Paterson, Randall J. Sobie, Ryan Taylor
- "Auto-Scaling of Virtual Resources for Scientific Workflows on Hybrid Clouds", Younsun Ahn, Yoonhee Kim
- "A Distributed Architecture for Intra- and Inter- Cloud Data Management", Ian Kelley
- Deadline extension: Abstracts due Monday March 3rd, and papers due Monday March 10th!
- 4th Workshop on Scientific Cloud Computing (ScienceCloud) 2013
Overview
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
- Abstract Submission: March 3rd, 2014
- Paper submission: March 1st, 2014 March 10th, 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-templates). A 250 word abstract must be submitted online at https://cmt.research.microsoft.com/SCIENCECLOUD2014/ prior to March 3rd, 2014; the full paper in PDF format should be submitted before March 10th, 2014. 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. 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, 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
Sponsors
TBA