Robot Limited

Data Cloud Design


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. 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.

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
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