Multiresource allocation: Fairness-efficiency tradeoffs in a unifying framework

Carlee Joe-Wong, Soumya Sen, Tian Lan, Mung Chiang

Research output: Contribution to journalArticlepeer-review

131 Scopus citations


Quantifying the notion of fairness is underexplored when there are multiple types of resources and users request different ratios of the different resources. A typical example is data centers processing jobs with heterogeneous resource requirements on CPU, memory, network bandwidth, etc. In such cases, a tradeoff arises between equitability, or 'fairness,' and efficiency. This paper develops a unifying framework addressing the fairness-efficiency tradeoff in light of multiple types of resources. We develop two families of fairness functions that provide different tradeoffs, characterize the effect of user requests' heterogeneity, and prove conditions under which these fairness measures satisfy the Pareto efficiency, sharing incentive, and envy-free properties. Intuitions behind the analysis are explained in two visualizations of multiresource allocation. We also investigate people's fairness perceptions through an online survey of allocation preferences.

Original languageEnglish (US)
Article number6403598
Pages (from-to)1785-1798
Number of pages14
JournalIEEE/ACM Transactions on Networking
Issue number6
StatePublished - Dec 2013


  • Data center
  • efficiency
  • fairness
  • multiresource allocation
  • resource allocation
  • tradeoff


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