Multi-resource allocation: Fairness-efficiency tradeoffs in a unifying framework

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

100 Scopus citations

Abstract

Quantifying the notion of fairness is under-explored when users request different ratios of multiple distinct resource types. A typical example is datacenters processing jobs with heterogeneous resource requirements on CPU, memory, etc. A generalization of max-min fairness to multiple resources was recently proposed in [1], but may suffer from significant loss of efficiency. This paper develops a unifying framework addressing this fairness-efficiency tradeoff with multiple resource types. We develop two families of fairness functions which 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 multi-resource allocation.

Original languageEnglish (US)
Title of host publication2012 Proceedings IEEE INFOCOM, INFOCOM 2012
Pages1206-1214
Number of pages9
DOIs
StatePublished - Jun 4 2012
EventIEEE Conference on Computer Communications, INFOCOM 2012 - Orlando, FL, United States
Duration: Mar 25 2012Mar 30 2012

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X

Other

OtherIEEE Conference on Computer Communications, INFOCOM 2012
CountryUnited States
CityOrlando, FL
Period3/25/123/30/12

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multi-resource allocation: Fairness-efficiency tradeoffs in a unifying framework'. Together they form a unique fingerprint.

Cite this