Multi-Resource Fairness: Objectives, Algorithms and Performance - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

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 .
Fichier principal
Vignette du fichier
paper.pdf (386.72 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01243985 , version 1 (15-12-2015)

Identifiers

  • HAL Id : hal-01243985 , version 1

Cite

Thomas Bonald, James Roberts. Multi-Resource Fairness: Objectives, Algorithms and Performance. ACM Sigmetrics, 2015, Portland, United States. ⟨hal-01243985⟩
352 View
373 Download

Share

Gmail Facebook X LinkedIn More