Cart (Loading....) | Create Account
Close category search window
 

Extended forecast of CPU and network load on computational Grid

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

2 Author(s)
Akioka, S. ; Sci. & Eng. Sch., Waseda Univ., Tokyo, Japan ; Muraoka, Y.

To achieve effective load balancing and a robust Grid environment, extended load forecast for computational resources is increasingly required. Thus, this paper proposes a method of predicting network and CPU load variance within a wide range, from several minutes to over a week. This is the widest range of prediction of the existing algorithms in the load of computational resources for the Grid environment. The distinctiveness of our algorithm is in using seasonal load variation for both load variance and one-step-ahead prediction. We apply seasonal fluctuation in CPU load to network load variation especially for network load variance prediction. Furthermore, the Markov model-based meta-predictor is used for one-step-ahead prediction, which is sensitive to late trends. The results of the experiments demonstrate that our algorithm gives a good curve for expected 8-day-long load variance, and makes accurate one-step-ahead predictions. The mean error rate for one-step-ahead predictions is 9.4% in the case of network load, and 6.2% in the case of CPU load. Moreover, the least mean error rate for wider range forecasts is 5.5% for network load variation, and 3.6% for CPU load variation.

Published in:

Cluster Computing and the Grid, 2004. CCGrid 2004. IEEE International Symposium on

Date of Conference:

19-22 April 2004

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.