New Algorithm for the Generalized Max-Min Fairness Policy Based on Linear Programming

Ioannis D. MOSCHOLIOS; Michael D. LOGOTHETIS

doi:10.1093/ietcom/e88-b.2.775

New Algorithm for the Generalized Max-Min Fairness Policy Based on Linear Programming

Ioannis D. MOSCHOLIOS, Michael D. LOGOTHETIS

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The Generalized Max-Min Fairness policy (GMM) allocates in a fair way the available bandwidth among elastic calls by taking into account their minimum and maximum rate requirements. The GMM has been described in a five-step procedure, which has the advantage of an easy presentation, but does not come into details, as far as its computer implementation is concerned, and fails to describe the policy in a clear mathematical way. We propose a new algorithm for the GMM policy, in a clear mathematical way, based on Linear Programming (LP). The new algorithm is directly convertible into software. Numerical examples clarify our algorithm.

Publication: IEICE TRANSACTIONS on Communications Vol.E88-B No.2 pp.775-780

Publication Date: 2005/02/01

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DOI: 10.1093/ietcom/e88-b.2.775

Type of Manuscript: LETTER

Category: Network

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Ioannis D. MOSCHOLIOS, Michael D. LOGOTHETIS, "New Algorithm for the Generalized Max-Min Fairness Policy Based on Linear Programming" in IEICE TRANSACTIONS on Communications, vol. E88-B, no. 2, pp. 775-780, February 2005, doi: 10.1093/ietcom/e88-b.2.775.
Abstract: The Generalized Max-Min Fairness policy (GMM) allocates in a fair way the available bandwidth among elastic calls by taking into account their minimum and maximum rate requirements. The GMM has been described in a five-step procedure, which has the advantage of an easy presentation, but does not come into details, as far as its computer implementation is concerned, and fails to describe the policy in a clear mathematical way. We propose a new algorithm for the GMM policy, in a clear mathematical way, based on Linear Programming (LP). The new algorithm is directly convertible into software. Numerical examples clarify our algorithm.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e88-b.2.775/_p

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@ARTICLE{e88-b_2_775,
author={Ioannis D. MOSCHOLIOS, Michael D. LOGOTHETIS, },
journal={IEICE TRANSACTIONS on Communications},
title={New Algorithm for the Generalized Max-Min Fairness Policy Based on Linear Programming},
year={2005},
volume={E88-B},
number={2},
pages={775-780},
abstract={The Generalized Max-Min Fairness policy (GMM) allocates in a fair way the available bandwidth among elastic calls by taking into account their minimum and maximum rate requirements. The GMM has been described in a five-step procedure, which has the advantage of an easy presentation, but does not come into details, as far as its computer implementation is concerned, and fails to describe the policy in a clear mathematical way. We propose a new algorithm for the GMM policy, in a clear mathematical way, based on Linear Programming (LP). The new algorithm is directly convertible into software. Numerical examples clarify our algorithm.},
keywords={},
doi={10.1093/ietcom/e88-b.2.775},
ISSN={},
month={February},}

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TY - JOUR
TI - New Algorithm for the Generalized Max-Min Fairness Policy Based on Linear Programming
T2 - IEICE TRANSACTIONS on Communications
SP - 775
EP - 780
AU - Ioannis D. MOSCHOLIOS
AU - Michael D. LOGOTHETIS
PY - 2005
DO - 10.1093/ietcom/e88-b.2.775
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E88-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2005
AB - The Generalized Max-Min Fairness policy (GMM) allocates in a fair way the available bandwidth among elastic calls by taking into account their minimum and maximum rate requirements. The GMM has been described in a five-step procedure, which has the advantage of an easy presentation, but does not come into details, as far as its computer implementation is concerned, and fails to describe the policy in a clear mathematical way. We propose a new algorithm for the GMM policy, in a clear mathematical way, based on Linear Programming (LP). The new algorithm is directly convertible into software. Numerical examples clarify our algorithm.
ER -