[hpc-announce] [Deadline Extended to Sept 12th, 2014] DataCloud-2014 at SC14: 5th International Workshop on Data Intensive Computing in the Clouds

[Wei Tang]

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Due to multiple requests, the deadline of DataCloud 2014 has been extended
to Sept 12th, 2014. Following is the updated CFP.

DataCloud 2014: 5th International Workshop on Data Intensive Computing in
the Clouds

In conjunction with SC14
In cooperation with ACM SIGHPC

November 21st, 2014
New Orleans LA, USA, New Orleans, LA

**URL: http://datasys.cs.iit.edu/events/DataCloud2014

**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 technologies 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 bigdata applications.

DataCloud 2014 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 2014
workshop will focus on the use ofcloud-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 deadline: September 12th, 2014 (11:59pm ET)***
Acceptance notification: October 1st, 2014
Camera ready papers due: October 10th, 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, 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://cmt.research.microsoft.com/DATACLOUD2014/. 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. For more information, please see
http://datasys.cs.iit.edu/events/DataCloud2014/.

**Organization

**General Chairs

Wei Tang, Argonne National Laboratory, USA
Yong Zhao, University of Electronic Science and Technology of China, China
Ziming Zheng, HP Vertica, USA

**Steering Committee

Roger Barga, Microsoft Research, USA
Ian Foster, University of Chicago & Argonne National Laboratory, USA
Geoffrey Fox, Indiana University, USA
James Hamilton, Amazon, USA
Tevfik Kosar, University at Buffalo, USA
Manish Parashar, Rutgers University, USA
Ioan Raicu, Illinois Institute of Technology & Argonne National Laboratory,
USA
Dan Reed, Microsoft Research, USA
Rich Wolski, University of California at Santa Barbara, USA
Rong Chang, IBM, USA

**Program Committee

Abhishek Chandra, University of Minnesota, USA
Bernard Traversat, Oracle Corporation, USA
Bingsheng He, Nanyang Technogical University, Singapore
Dan Katz, University of Chicago, USA
Dong Li, Oak Ridge National Laboratory, USA
Douglas Thain,University of Notre Dame,USA
Erwin Laure, CERN, Switzerland
Geoffrey Fox, Indiana University, USA
Hangwei Qian,VMWare, USA
Hongbo Zou, VMWare, USA
Ian Foster, University of Chicago and Argonne National Laboratory, USA
Jim Myers, Rensselaer Polytechnic Institute, USA
Judy Qiu, Indiana University, USA
Kate Keahey, Argonne National Laboratory, USA
Kyle Chard, University of Chicago, USA
Lavanya Ramakrishnan, Lawrence Berkeley National Laboratory, USA
Maria Indrawan, Monash University, Australia
Matei Ripeanu, University of British Columbia, Canada
Murat Demirbas, SUNY Buffalo, USA
Narayan Desai, Ericcson, USA
Qiang Guan, Los Alamos National Laboratory, USA
Reagan Moore, University of North Carolina at Chapel Hill, USA
Rob Gillen, Oak Ridge National Laboratory, USA
Ruini Xue, University of Electronic Science and Technology, China
Samer Al-Kiswany, University of British Columbia, Canada
Steven Ko, SUNY Buffalo, USA
Teng Ma, Amazon, USA
Venkat Vishwanath, Argonne National Laboratory, USA
Xiaoliang Fan, Lanzhou University, China
Zhen Liu, Beijing Jiaotong University, China
Zhiang Wu, Nanjing University of Finance and Economics, China
Zhifeng Yun, University of Houston, USA
Zhihui Lu, Fudan University, China
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