By Topic

Applying neural networks to computer system performance tuning

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

1 Author(s)
Bigus, J.P. ; IBM Corp., Rochester, MN, USA

This paper presents results of empirical studies applying neural networks and techniques from control systems theory to computer system performance tuning. Experiments were performed on a simulated multiprogrammed computer system with a time-varying workload comprising four job classes. Key system performance measures such as device utilizations, mean queue lengths, and paging rates were collected and used to train neural network performance models. Several model-based adaptive control experiments show that backpropagation and radial basis function neural network controllers can be trained online to adjust memory allocations in order to meet desired performance objectives

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:4 )

Date of Conference:

27 Jun-2 Jul 1994