Multi-Resource Fairness: Objectives, Algorithms and Performance

Thomas Bonald (1, 2) , James Roberts (3, 2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 LINCS - Laboratory of Information, Network and Communication Sciences
3 IRT SystemX

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 .

Mots clés: Dominant resource fairness; proportional fairness; bottleneck max fairness; multi-resource sharing; cluster computing

Domaines: Réseaux et télécommunications [cs.NI]; Performance et fiabilité [cs.PF]

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