Multi-Resource Allocation: Fairness-Efficiency Tradeoffs in a Unifying Framework

被引:0
|
作者
Joe-Wong, Carlee [1 ]
Sen, Soumya [1 ]
Lan, Tian [2 ]
Chiang, Mung [1 ]
机构
[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
[2] George Washington Univ, Dept Elect & Comp Engn, Washington, DC 20052 USA
来源
2012 PROCEEDINGS IEEE INFOCOM | 2012年
关键词
NETWORKS;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
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收藏
页码:1206 / 1214
页数:9
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