[hpc-announce] [sc-workshop-attendee-cfp] [Deadline extension] DataCloud17 at SC17 - The Eighth International Workshop on Data-Intensive Computing in the Clouds

[Li, Tonglin]

Call for Papers (deadline extended)
DataCloud 2017: The Eighth International Workshop on Data-Intensive Computing in the Clouds
In conjunction with SC17, Denver, CO, USA, November 12, 2017
Workshop website     https://sites.google.com/view/2017datacloud/home
Submission link  https://easychair.org/conferences/?conf=datacloud2017
Submission deadline is extended to September 15, 2017
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. As scientific applications become more data intensive, the management of data resources and data flow between the storage and compute resources is becoming the main bottleneck. 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.
The eighth international workshop on Data-intensive Computing in the Clouds (DataCloud 2017) 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 2017 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.
Submission Guidelines
List of Topics
- Data-intensive cloud computing infrastructure, applications, characteristics and 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
- Full paper submission: Sep 15st, 2017
- Acceptance date: Oct 7st, 2017
- Camera ready: Oct 15th, 2017
Submission Guidelines
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/publications/proceedings-templates. The final papers in PDF format must be submitted online at https://easychair.org/conferences/?conf=datacloud2017. Papers will be peer-reviewed, and accepted papers will be published in the workshop proceedings as part of the ACM digital library (in cooperation with SIGHPC). Submission implies the willingness of at least one of the authors to register and present the paper.
Program Chairs
- Tonglin Li,  Oak Ridge National Laboratory
- Boyu Zhang,  Microsoft Inc.
- Xuan Guo,  Oak Ridge National Laboratory
Steering Committee
- Wei Tang,  Google Inc.
- Dongfang Zhao, University of Nevada, Reno
- Roger Barga,  Microsoft Research
- Ian Foster,  University of Chicago & ANL
- Geoffrey Fox,  Indiana University
All questions about submissions should be emailed to lit1 at ornl.gov, zhang.boyu84 at gmail.com or guox at ornl.gov.
--
Tonglin Li, Ph.D.
Computer Science and Mathematics Division
Oak Ridge National Laboratory

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