@Article{oldfield:2006:restruct,
  author = {Ron Oldfield and David Kotz},
  title = {Improving data access for computational grid applications},
  journal = {Cluster Computing, The Journal of Networks, Software Tools and
  Applications},
  year = {2006},
  month = {January},
  volume = {9},
  number = {1},
  pages = {79--99},
  copyright = {the authors},
  category = {pario},
  DOI = {10.1007/s10586-006-4899-7},
  keywords = {parallel I/O, Grid computing, distributed computing, graph
  algorithms, pario-bib},
  abstract = {High-performance computing increasingly occurs on ``computational
  grids'' composed of heterogeneous and geographically distributed systems of
  computers, networks, and storage devices that collectively act as a single
  ``virtual'' computer. A key challenge in this environment is to provide
  efficient access to data distributed across remote data servers. Our parallel
  I/O framework, called Armada, allows application and data-set providers to
  flexibly compose graphs of processing modules that describe the distribution,
  application interfaces, and processing required of the dataset before
  computation. Although the framework provides a simple programming model for
  the application programmer and the data-set provider, the resulting graph may
  contain bottlenecks that prevent efficient data access. In this paper, we
  present an algorithm used to restructure Armada graphs that distributes
  computation and data flow to improve performance in the context of a
  wide-area computational grid.}
}