@Article{sun:dynamic,
  author = {Weitao T. Sun and Jiwu W. Shu and Weimin M. Zheng},
  title = {Dynamic file allocation in Storage Area Networks with neural network
  prediction},
  journal = {Lecture Notes in Computer Science},
  booktitle = {International Symposium on Neural Networks (ISSN 2004); August
  19-21, 2004; Dalian, PEOPLES R CHINA},
  editor = {Yin, FL; Wang, J; Guo, CG},
  year = {2004},
  month = {June},
  volume = {3174},
  pages = {719--724},
  institution = {Tsing Hua Univ, Dept Comp Sci \& Technol, Beijing 100084,
  Peoples R China},
  publisher = {Springer-Verlag Heidelberg},
  copyright = {(c)2004 Institute for Scientific Information, Inc.},
  URL = {http://www.springerlink.com/link.asp?id=7t97qycr7awnbw6j},
  keywords = {SAN, dynamic data reorganization, neural network, access pattern
  prediction, pario-bib},
  abstract = {Disk arrays are widely used in Storage Area Networks (SANs) to
  achieve mass storage capacity and high level I/O parallelism. Data
  partitioning and distribution among the disks is a promising approach to
  minimize the file access time and balance the I/O workload. But disk I/O
  parallelism by itself does not guarantee the optimal performance of an
  application. The disk access rates fluctuate with time because of access
  pattern variations, which leads to a workload imbalance. The user access
  pattern prediction is of great importance to dynamic data reorganization
  between hot and cool disks. Data migration occurs according to current and
  future disk allocation states and access frequencies. The objective of this
  paper is to develop a neural network based disk allocation trend prediction
  method and optimize the disks' file capacity to their balanced level. A
  Levenberg-Marquardt neural network was adopted to predict the disk access
  frequencies with the I/O track. History. Data reorganization on disk arrays
  was optimized to provide a good workload balance. The simulation results
  proved that the proposed method performs well.}
}