@InProceedings{hidrobo:autonomic,
  author = {Francisco Hidrobo and Toni Cortes},
  title = {Towards an autonomic storage system to improve parallel {I/O}.},
  booktitle = {Proceedings of the 15th IASTED International Conference on
  Parallel and Distributed Computing and Systems},
  year = {2003},
  month = {November},
  pages = {122-127, vol 1},
  publisher = {ACTA Press},
  copyright = {(c)2004 IEE},
  address = {Marina del Rey, CA},
  keywords = {performance prediction, data placement, storage device modeling,
  parallel I/O, pario-bib},
  abstract = {In this paper, we present a mechanism able to predict the
  performance a given workload will achieve when running on a given storage
  device. This mechanism is composed by two modules. The first one is able to
  reproduce its behavior later on, without a new execution, even when the
  storage drives or data placement are modified. The second module is a drive
  modeler that is able to learn how storage drive works in an automatic way,
  just executing some synthetic tests. Once we have the workload and drive
  models, we can predict how well that application will perform on the selected
  storage device or devices or when the data placement is modified. The results
  presented in this paper will show that this prediction system achieves errors
  below 10% when compared to the real performance obtained. It is important to
  notice that the two modules will treat both the application and the storage
  device as black and will need no previous information about them. (20
  refs.)},
  comment = {Could not find a URL. See
  <http://www.actapress.com/proceedings/pdcs.htm> for proceedings information.}
}