Ali Ghodsi and Matei Zaharia and Benjamin Hindman and Andrew Konwinski and Scott Shenker and Ion Stoica

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2010-55

May 7, 2010

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-55.pdf

This paper investigates how different resources can be fairly allocated among users that possibly prioritize them differently. We introduce a fairness policy, called Dominant Resource Fairness (DRF), which is an adaptation of max-min fairness from networking to datacenter environments. We show that DRF, unlike other policies which we investigated, satisfies a number of desirable properties that a fair datacenter scheduler should have, including guaranteeing that every user gets 1/N of some resource and that users can relinquish resources without hurting other users’ allocations. DRF is also envy-free, incentivizing users to correctly report their resource demand. When compared to other intuitive schedulers, as well as competing ones from microeconomic theory, DRF is more fair.

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BibTeX citation:

@techreport{Ghodsi:EECS-2010-55,
    Author= {Ghodsi, Ali and Zaharia, Matei and Hindman, Benjamin and Konwinski, Andrew and Shenker, Scott and Stoica, Ion},
    Title= {Dominant Resource Fairness: Fair Allocation of Heterogeneous Resources in Datacenters},
    Year= {2010},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-55.html},
    Number= {UCB/EECS-2010-55},
    Abstract= {This paper investigates how different resources can be fairly allocated among users that possibly prioritize them differently. We introduce a fairness policy, called Dominant Resource Fairness (DRF), which is an adaptation of max-min fairness from networking to datacenter environments. We show that DRF, unlike other policies which we investigated, satisfies a number of desirable properties that a fair datacenter scheduler should have, including guaranteeing that every user gets 1/N of some resource and that users can relinquish resources without hurting other users’ allocations. DRF is also envy-free, incentivizing users to correctly report their resource demand. When compared to other intuitive schedulers, as well as competing ones from microeconomic theory, DRF is more fair.},
}

EndNote citation:

%0 Report
%A Ghodsi, Ali 
%A Zaharia, Matei 
%A Hindman, Benjamin 
%A Konwinski, Andrew 
%A Shenker, Scott 
%A Stoica, Ion 
%T Dominant Resource Fairness: Fair Allocation of Heterogeneous Resources in Datacenters
%I EECS Department, University of California, Berkeley
%D 2010
%8 May 7
%@ UCB/EECS-2010-55
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-55.html
%F Ghodsi:EECS-2010-55