@InProceedings{madhyastha:classification,
  author = {Tara M. Madhyastha and Daniel A. Reed},
  title = {Input/Output Access Pattern Classification Using Hidden {Markov}
  Models},
  booktitle = {Proceedings of the Fifth Workshop on Input/Output in Parallel
  and Distributed Systems},
  year = {1997},
  month = {November},
  pages = {57--67},
  publisher = {ACM Press},
  address = {San Jose, CA},
  later = {madhyastha:thesis},
  URL = {http://doi.acm.org/10.1145/266220.266226},
  keywords = {workload characterization, file access pattern, parallel I/O,
  pario-bib},
  abstract = {Input/output performance on current parallel file systems is
  sensitive to a good match of application access pattern to file system
  capabilities. Automatic input/output access classification can determine
  application access patterns at execution time, guiding adaptive file system
  policies. In this paper we examine a new method for access pattern
  classification that uses hidden Markov models, trained on access patterns
  from previous executions, to create a probabilistic model of input/output
  accesses. We compare this approach to a neural network classification
  framework, presenting performance results from parallel and sequential
  benchmarks and applications.},
  comment = {The most interesting thing in this paper is the use of a Hidden
  Markov Model to understand the access pattern of an application to a file.
  After running the application on the file once, and simultaneously training
  their HMM, they use the result to tune the system for the next execution
  (cache size, cache partitioning, prefetching, Intel file mode, etc). They get
  much better performance in future runs. See also madhyastha:thesis, and
  related papers.}
}