DataCloud 2016: The Seventh International Workshop on Data-Intensive Computing in the Clouds

In conjunction with SC16. Salt Lake City, Utah -- November 14, 2016
In cooperation with ACM SIGHPC.

New: Workshop Program is announced, click here


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 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.

For more information about the previous DataCloud workshops, please see Past Workshops.


  • 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