Multi-Resource Fairness: Objectives, Algorithms and Performance

Abstract : Designing efficient and fair algorithms for sharing multiple resources between heterogeneous demands is becoming increasingly important. Applications include compute clusters shared by multi-task jobs and routers equipped with middle-boxes shared by flows of different types. We show that the currently preferred objective of Dominant Resource Fairness (DRF) has a significantly less favorable efficiency-fairness tradeoff than alternatives like Proportional Fairness and our proposal, Bottleneck Max Fairness. We propose practical algorithms to realize these sharing objectives and evaluate their performance under a stochastic demand model. It is shown, in particular, that the strategyproofness property that motivated the choice of DRF for an assumed fixed set of jobs or flows, is largely irrelevant when demand is dynamic .
Type de document :
Communication dans un congrès
ACM Sigmetrics, 2015, Portland, United States. Proceedings of ACM Sigmetrics, ACM Sigmetrics
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01243985
Contributeur : Thomas Bonald <>
Soumis le : mardi 15 décembre 2015 - 13:40:41
Dernière modification le : samedi 18 février 2017 - 01:17:42
Document(s) archivé(s) le : samedi 29 avril 2017 - 15:07:26

Fichier

paper.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01243985, version 1

Citation

Thomas Bonald, James Roberts. Multi-Resource Fairness: Objectives, Algorithms and Performance. ACM Sigmetrics, 2015, Portland, United States. Proceedings of ACM Sigmetrics, ACM Sigmetrics. 〈hal-01243985〉

Partager

Métriques

Consultations de
la notice

239

Téléchargements du document

133