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Call for Papers: DataCloud 2016: 7th International Workshop on Data Intensive Computing in the Clouds
In conjunction with SC16
In cooperation with ACM SIGHPC
November 14th, 2016
Salt Lake City, UT
URL: https://www.rcac.purdue.e
** Overview
Applications and experiments in all areas of science are becoming increasingly complex and more demanding in terms of their computational and data requirements. Some applications generate data volumes reaching hundreds of terabytes and even petabytes. Analyzing, visualizing, and disseminating these large data sets has become a major challenge and data intensive computing is now considered as the ''fourth paradigm'' in scientific discovery after theoretical, experimental, and computational science.
As scientific applications become more data intensive, the technology of handling "Big Data" have gathered great importance. This necessity has made that applications have seen an increasing adoption on clouds infrastructures. The computing models,system software, programming models, analysis frameworks, and other clouds services need to evolve and accommodate them to face the challenge of big data applications.
DataCloud 2016 will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running data-intensive computing workloads on Cloud Computing infrastructures. The DataCloud 2016 workshop will focus on the use of cloud-based technologies to meet the new data intensive scientific challenges that are not well served by the current supercomputers, grids or compute-intensive clouds. We believe the workshop will be an excellent place to help the community define the current state, determine future goals, and present architectures and services for future clouds supporting data intensive computing.
** Topics
Big data analytics
Data-intensive cloud computing applications, characteristics, challenges
Case studies of data intensive computing in the clouds
Performance evaluation of data clouds, data grids, and data centers
Energy-efficient data cloud design and management
Data placement, scheduling, and interoperability in the clouds
Accountability, QoS, and SLAs
Data privacy and protection in a public cloud environment
Distributed file systems for clouds
Data streaming and parallelization
New programming models for data-intensive cloud computing
Scalability issues in clouds
Social computing and massively social gaming
3D Internet and implications
Future research challenges in data-intensive cloud computing
** Important Dates
Paper submission: Aug 26th, 2016
Acceptance notification: October 1st, 2016
Final papers due: October 10th, 2016
** 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, as per ACM 8.5 x 11 manuscript guidelines; document templates can be found at http://www.acm.org/sigs/pub
** General Chairs
Wei Tang, Google, USA
Boyu Zhang, Purdue University, USA
Yong Zhao, University of Electronic Science and Technology of China, China
** Program Committee (To be confirmed)
Abhishek Chandra, University of Minnesota, USA
Bernard Traversat, Oracle Corporation, USA
Bingsheng He, National University of Singapore, Singapore
Daniel S. Katz, University of Chicago & Argonne National Laboratory, USA
Dong Li, Oak Ridge National Laboratory, USA
Douglas Thain,University of Notre Dame,USA
Erwin Laure, CERN, Switzerland
Geoffrey Fox, Indiana University, USA
Ian Foster, University of Chicago and Argonne National Laboratory, USA
Jim Myers, Rensselaer Polytechnic Institute, USA
Judy Qiu, Indiana University, USA
Kyle Chard, University of Chicago, USA
Maria Indrawan, Monash University, Australia
Matei Ripeanu, University of British Columbia, Canada
Murat Demirbas, SUNY Buffalo, USA
Narayan Desai, Ericsson USA
Reagan Moore, University of North Carolina at Chapel Hill, USA
Rob Gillen, Oak Ridge National Laboratory
Samer Al-Kiswany, University of British Columbia, Canada
Steven Ko, SUNY Buffalo, USA
Xiaoliang Fan, Lanzhou University, China
Zhen Liu, Beijing Jiaotong University, China
Li Yu, Google Inc, USA
Xin Zhao, Mellanox Technologies, USA
Min Si, Argonne National Laboratory, USA
In conjunction with SC16
In cooperation with ACM SIGHPC
November 14th, 2016
Salt Lake City, UT
URL: https://www.rcac.purdue.e
** Overview
Applications and experiments in all areas of science are becoming increasingly complex and more demanding in terms of their computational and data requirements. Some applications generate data volumes reaching hundreds of terabytes and even petabytes. Analyzing, visualizing, and disseminating these large data sets has become a major challenge and data intensive computing is now considered as the ''fourth paradigm'' in scientific discovery after theoretical, experimental, and computational science.
As scientific applications become more data intensive, the technology of handling "Big Data" have gathered great importance. This necessity has made that applications have seen an increasing adoption on clouds infrastructures. The computing models,system software, programming models, analysis frameworks, and other clouds services need to evolve and accommodate them to face the challenge of big data applications.
DataCloud 2016 will provide the scientific community a dedicated forum for discussing new research, development, and deployment efforts in running data-intensive computing workloads on Cloud Computing infrastructures. The DataCloud 2016 workshop will focus on the use of cloud-based technologies to meet the new data intensive scientific challenges that are not well served by the current supercomputers, grids or compute-intensive clouds. We believe the workshop will be an excellent place to help the community define the current state, determine future goals, and present architectures and services for future clouds supporting data intensive computing.
** Topics
Big data analytics
Data-intensive cloud computing applications, characteristics, challenges
Case studies of data intensive computing in the clouds
Performance evaluation of data clouds, data grids, and data centers
Energy-efficient data cloud design and management
Data placement, scheduling, and interoperability in the clouds
Accountability, QoS, and SLAs
Data privacy and protection in a public cloud environment
Distributed file systems for clouds
Data streaming and parallelization
New programming models for data-intensive cloud computing
Scalability issues in clouds
Social computing and massively social gaming
3D Internet and implications
Future research challenges in data-intensive cloud computing
** Important Dates
Paper submission: Aug 26th, 2016
Acceptance notification: October 1st, 2016
Final papers due: October 10th, 2016
** 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, as per ACM 8.5 x 11 manuscript guidelines; document templates can be found at http://www.acm.org/sigs/pub
** General Chairs
Wei Tang, Google, USA
Boyu Zhang, Purdue University, USA
Yong Zhao, University of Electronic Science and Technology of China, China
** Program Committee (To be confirmed)
Abhishek Chandra, University of Minnesota, USA
Bernard Traversat, Oracle Corporation, USA
Bingsheng He, National University of Singapore, Singapore
Daniel S. Katz, University of Chicago & Argonne National Laboratory, USA
Dong Li, Oak Ridge National Laboratory, USA
Douglas Thain,University of Notre Dame,USA
Erwin Laure, CERN, Switzerland
Geoffrey Fox, Indiana University, USA
Ian Foster, University of Chicago and Argonne National Laboratory, USA
Jim Myers, Rensselaer Polytechnic Institute, USA
Judy Qiu, Indiana University, USA
Kyle Chard, University of Chicago, USA
Maria Indrawan, Monash University, Australia
Matei Ripeanu, University of British Columbia, Canada
Murat Demirbas, SUNY Buffalo, USA
Narayan Desai, Ericsson USA
Reagan Moore, University of North Carolina at Chapel Hill, USA
Rob Gillen, Oak Ridge National Laboratory
Samer Al-Kiswany, University of British Columbia, Canada
Steven Ko, SUNY Buffalo, USA
Xiaoliang Fan, Lanzhou University, China
Zhen Liu, Beijing Jiaotong University, China
Li Yu, Google Inc, USA
Xin Zhao, Mellanox Technologies, USA
Min Si, Argonne National Laboratory, USA
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