Dr. Darrell Long

Increasing predictive accuracy through limited prefetching

Proceedings of Communications Networks and Distributed Systems Modeling and Simulation (CNDS)

January 2002View full paper

Abstract

Prefetching multiple files per prediction can improve the predictive accuracy. However, it comes with the cost of using extra cache space and disk bandwidth. This paper discusses the most Recent distinct Successor (RnS) model and uses it to demonstrate the effectiveness of our earlier work, Program-based Last Successor (PLnS) model, a program-based prediction algorithm [21]. We analyze the simulation results from different trace data and show that PLnS can perform better than RnS while it only predicts at most 59% of the number of files predicted by RnS when the n in PLnS equals to two. PLnS is a good candidate when considering prefetching multiple files per prediction to improve predictive accuracy.

BibTeX

@inproceedings{yeh-cnds02,
  author       = {Tsozen Yeh and Darrell D. E. Long and Scott A. Brandt},
  title        = {Increasing Predictive Accuracy through Limited Prefetching},
  booktitle    = {Proceedings of Communications Networks and Distributed Systems Modeling and Simulation (CNDS)},
  organization = {Society for Computer Simulation},
  address      = {San Antonio},
  pages        = {131--138},
  month        = jan,
  year         = {2002}
}
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