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Achieving per-flow fair rate allocation in Diffserv

Achieving per-flow fair rate allocation in Diffserv This article addresses the fundamental issue of providing per-flow fairness. In particular, it focuses on fairness within the Diffserv framework. We propose the Fair Allocation Derivative Estimation (FADE) algorithm for estimating flow fair share in the absence of per-flow information. FADE calculates fair share feedback using a modified quasi-Newton method. This efficient method for estimating fair share provides a more precise model than other existing fairness estimation approaches. As such, it is able to more accurately estimate fair share and quickly converge to the proper rate. The simulation compares FADE to other proposals. The results demonstrate the overall effectiveness of the algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Modeling and Computer Simulation (TOMACS) Association for Computing Machinery

Achieving per-flow fair rate allocation in Diffserv

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2001 by ACM Inc.
ISSN
1049-3301
DOI
10.1145/384169.384171
Publisher site
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Abstract

This article addresses the fundamental issue of providing per-flow fairness. In particular, it focuses on fairness within the Diffserv framework. We propose the Fair Allocation Derivative Estimation (FADE) algorithm for estimating flow fair share in the absence of per-flow information. FADE calculates fair share feedback using a modified quasi-Newton method. This efficient method for estimating fair share provides a more precise model than other existing fairness estimation approaches. As such, it is able to more accurately estimate fair share and quickly converge to the proper rate. The simulation compares FADE to other proposals. The results demonstrate the overall effectiveness of the algorithm.

Journal

ACM Transactions on Modeling and Computer Simulation (TOMACS) – Association for Computing Machinery

Published: Apr 1, 2001

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