@InProceedings{madhyastha:adaptive,
  author = {Tara M. Madhyastha and Daniel A. Reed},
  title = {Intelligent, Adaptive File System Policy Selection},
  booktitle = {Proceedings of the Sixth Symposium on the Frontiers of Massively
  Parallel Computation},
  year = {1996},
  month = {October},
  pages = {172--179},
  publisher = {IEEE Computer Society Press},
  later = {madhyastha:thesis},
  keywords = {parallel I/O, pario-bib},
  abstract = {Traditionally, maximizing input/output performance has required
  tailoring application input/output patterns to the idiosyncrasies of specific
  input/output systems. The authors show that one can achieve high application
  input/output performance via a low overhead input/output system that
  automatically recognizes file access patterns and adaptively modifies system
  policies to match application requirements. This approach reduces the
  application developer's input/output optimization effort by isolating
  input/output optimization decisions within a retargetable file system
  infrastructure. To validate these claims, they have built a lightweight file
  system policy testbed that uses a trained learning mechanism to recognize
  access patterns. The file system then uses these access pattern
  classifications to select appropriate caching strategies, dynamically
  adapting file system policies to changing input/output demands throughout
  application execution. The experimental data show dramatic speedups on both
  benchmarks and input/output intensive scientific applications.},
  comment = {See also madhyastha:thesis, and related papers.}
}