@Article{tran:jadaptive,
  author = {Nancy Tran and Daniel A. Reed},
  title = {Automatic {ARIMA} Time Series Modeling for Adaptive {I/O}
  Prefetching},
  journal = {IEEE Transactions on Parallel and Distributed Systems},
  year = {2004},
  month = {April},
  volume = {15},
  number = {4},
  pages = {362--377},
  publisher = {IEEE Computer Society Press},
  earlier = {tran:adaptive},
  URL = {http://csdl.computer.org/dl/trans/td/2004/04/l0362.pdf},
  keywords = {pario-bib, access pattern, prefetching, modeling, time-series
  analysis},
  abstract = {Inadequate I/O performance remains a major challenge in using
  high-end computing systems effectively. To address this problem, the paper
  presents TsModeler, an automatic time series modeling and prediction
  framework for adaptive I/O prefetching that uses ARIMA time series models to
  predict the temporal patterns of I/O requests. These online pattern analysis
  techniques and cutoff indicators for autocorrelation patterns enable
  multistep online predictions suitable for multiblock prefetching. This work
  also combines time series predictions with spatial Markov model predictions
  to determine when, what, and how many blocks to prefetch. Experimental
  results show reductions in execution time compared to the standard Linux file
  system across various hardware configurations.}
}