Community-driven data grids

  • Authors:
  • Tobias Scholl;Alfons Kemper

  • Affiliations:
  • Technische Universität München, Munich, Germany;Technische Universität München, Munich, Germany

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2008

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Abstract

Beyond already existing huge data volumes, e-science communities face major challenges in managing the anticipated data deluge of forthcoming projects. Community-driven data grids target at domain-specific federations and provide a distributed, collaborative data management by employing dominant data characteristics (e. g., data skew) and query patterns to optimize the overall throughput. By combining well-established techniques for data partitioning and replication with Peer-to-Peer (P2P) technologies we can address several challenging problems: data load balancing, handling of query hot spots, and the adaption to short-term burst as well as long-term load redistributions.