@InProceedings{arpaci-dusseau:river,
  author = {Remzi H. Arpaci-Dusseau and Eric Anderson and Noah Treuhaft and
  David E. Culler and Joseph M. Hellerstein and David Patterson and Kathy
  Yelick},
  title = {Cluster {I/O} with {River}: Making the Fast Case Common},
  booktitle = {Proceedings of the Sixth Workshop on Input/Output in Parallel
  and Distributed Systems},
  year = {1999},
  month = {May},
  pages = {10--22},
  publisher = {ACM Press},
  address = {Atlanta, GA},
  URL = {http://vibes.cs.uiuc.edu/IOPADS/Accepted/Remzi.ps},
  keywords = {cluster computing, parallel I/O, pario-bib},
  abstract = {We introduce River, a data-flow programming environment and I/O
  substrate for clusters of computers. River is designed to provide maximum
  performance in the common case--- even in the face of non-uniformities in
  hardware, software, and workload. River is based on two simple design
  features: a high-performance distributed queue,and a storage redundancy
  mechanism called graduated declustering.We have implemented a number of
  data-intensive applications on River, which validate our design with
  near-ideal performance in a variety of non-uniform performance scenarios.}
}