Workload characterization in a high-energy data grid and impact on resource management

  • Authors:

  • Adriana Iamnitchi;Shyamala Doraimani;Gabriele Garzoglio

  • Affiliations:

  • Computer Science and Engineering, University of South Florida, Tampa, USA 33620;Computer Science and Engineering, University of South Florida, Tampa, USA 33620;Computing Division, Fermi National Accelerator Laboratory, Batavia, USA 60510

  • Venue:
  • Cluster Computing
  • Year:
  • 2009

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Abstract

The analysis of data usage in a large set of real traces from a high-energy physics collaboration revealed the existence of an emergent grouping of files that we coined "filecules". This paper presents the benefits of using this file grouping for prestaging data and compares it with previously proposed file grouping techniques along a range of performance metrics. Our experiments with real workloads demonstrate that filecule grouping is a reliable and useful abstraction for data management in science Grids; that preserving time locality for data prestaging is highly recommended; that job reordering with respect to data availability has significant impact on throughput; and finally, that a relatively short history of traces is a good predictor for filecule grouping. Our experimental results provide lessons for workload modeling and suggest design guidelines for data management in data-intensive resource-sharing environments.