By Topic

Predicting when not to predict

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Brandt, K. ; California Univ., Santa Cruz, CA, USA ; Long, D.D.E. ; Amer, A.

File prefetching based on previous file access patterns has been shown to be an effective means of reducing file system latency by implicitly loading caches with files that are likely to be needed in the near future. Mistaken prefetching requests can be very costly in terms of added performance overheads, including increased latency and bandwidth consumption. Such costs of mispredictions are easily overlooked when considering access prediction algorithms only in terms of their accuracy; we describe a novel algorithm that uses machine learning not only to improve overall prediction accuracy, but also as a means to avoid those costly mispredictions. Our algorithm is fully adaptive to changing workloads, and is fully automated in its ability to refrain from offering predictions when they are likely to be mistaken. Our trace-based simulations show that our algorithm produces prediction accuracies of up to 98%. While this appears to be at the expense of a very slight reduction in cache hit ratios, application of this algorithm actually results in substantial reductions in unnecessary (and costly) I/O operations.

Published in:

Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings. The IEEE Computer Society's 12th Annual International Symposium on

Date of Conference:

4-8 Oct. 2004