Adaptive Caching by Refetching
Abstract
We are constructing caching policies that have 13-20% lower miss rates than the best of twelve baseline policies over a large variety of request streams. This represents an improvement of 49–63% over Least Recently Used, the most commonly implemented policy. We achieve this not by designing a specific new policy but by using on-line Machine Learning algorithms to dynamically shift between the standard policies based on their observed miss rates. A thorough experimental evaluation of our techniques is given, as well as a discussion of what makes caching an interesting on-line learning problem.
Cite
Text
Gramacy et al. "Adaptive Caching by Refetching." Neural Information Processing Systems, 2002.Markdown
[Gramacy et al. "Adaptive Caching by Refetching." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/gramacy2002neurips-adaptive/)BibTeX
@inproceedings{gramacy2002neurips-adaptive,
title = {{Adaptive Caching by Refetching}},
author = {Gramacy, Robert B. and Warmuth, Manfred K. and Brandt, Scott A. and Ari, Ismail},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1489-1496},
url = {https://mlanthology.org/neurips/2002/gramacy2002neurips-adaptive/}
}