@InCollection{durand:scheduling-book,
  author = {Dannie Durand and Ravi Jain and David Tseytlin},
  title = {Improving the Performance of Parallel {I/O} Using Distributed
  Scheduling Algorithms},
  booktitle = {Input/Output in Parallel and Distributed Computer Systems},
  chapter = {11},
  editor = {Ravi Jain and John Werth and James C. Browne},
  crossref = {iopads-book},
  year = {1996},
  series = {The Kluwer International Series in Engineering and Computer
  Science},
  volume = {362},
  pages = {245--269},
  publisher = {Kluwer Academic Publishers},
  earlier = {durand:scheduling},
  keywords = {parallel I/O, distributed scheduling algorithm, pario-bib},
  abstract = {The cost of data transfers, and in particular of I/O operations,
  is a growing problem in parallel computing. This performance bottleneck is
  especially severe for data-intensive applications such as multimedia
  information systems, databases, and Grand Challenge problems. A promising
  approach to alleviating this bottleneck is to schedule parallel I/O
  operations explicitly. \par Although centralized algorithms for batch
  scheduling of parallel I/O operations have previously been developed, they
  are not be appropriate for all applications and architectures. We develop a
  class of decentralized algorithms for scheduling parallel I/O operations,
  where the objective is to reduce the time required to complete a given set of
  transfers. These algorithms, based on edge-coloring and matching of bipartite
  graphs, rely upon simple heuristics to obtain shorter schedules. We present
  simulation results indicating that the best of our algorithms can produce
  schedules whose length (or makespan) is within 2 - 20\% of the optimal
  schedule, a substantial improvement on previous decentralized algorithms. We
  discuss theoretical and experimental work in progress and possible
  extensions.},
  comment = {Part of a whole book on parallel I/O; see iopads-book.}
}