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

Perturbation analysis for loss volume and buffer workload in tandem two-class stochastic fluid models for communication 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

2 Author(s)
Nosrati, S. ; Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran ; Nikravesh, S.K.

This paper applies infinitesimal perturbation analysis (IPA) to packet loss and buffer workload relative performance metrics in tandem networks of two-class stochastic fluid models (SFM). It considers these performance metrics at a given SFM node processing a single-flow class, as function of the controlled traffic stream's threshold parameter at an upstream SFM node processing two class of incoming traffic. It further supplements the existing results concerning applying IPA methods to tandem SFMs. The derived IPA gradient estimators are simple and fast to compute, and have an appealing property that are unbiased and nonparametric in the sense that they can be evaluated directly online from measurements of real-life traffic processes as well as offline from simulation experiments, without any knowledge of underlying stochastic characteristic of the traffic and service processes. These properties hold the promise of utilizing these IPA gradient estimators to network control and optimization.

Published in:

Control Conference, 2004. 5th Asian  (Volume:2 )

Date of Conference:

20-23 July 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.