By Topic

Predicting behavior patterns using adaptive workload models [computer networks]

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)
Raghavan, S.V. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Madras, India ; Swaminathan, N. ; Srinivasan, J.

Workload characteristics in a modern networking environment are very dynamic. In order to maximize performance continuously, it is natural to explore the possibility of intelligent systems which can take cognizance of the workload dynamics and adapt themselves for future control applications. In this paper, we propose a mechanism which represents the previous state of the system as a string. The user is allowed to define relevant information for better management as substrings. The adaptive workload model, which is called the SVR model (named after the first author's initials), predicts the short-term future as a string in which the information content (conveyed as a substring) reflects the future. We illustrate the applicability of the SVR model through Web traffic generation and ATM bandwidth management. We use genetic algorithms as the vehicle to address the learning aspects of the model

Published in:

Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 1999. Proceedings. 7th International Symposium on

Date of Conference: