An Axiomatic Theory of Fairness in Network Resource Allocation
By T. Lan, D. Kao, Mung Chiang, Ashutosh Sabharwal. 2009
444 citations recorded by the source corpus.
We present five axioms for fairness measures in resource allocation. A family of fairness measures satisfying the axioms is constructed. Special cases of this family include α-fairness, Jain's index, and entropy. Properties of fairness measures satisfying the axioms are proven, including Schur-concavity. Among the engineering implications is a generalized Jain's index that tunes the resolution of fairness measure, a new understanding of α-fair utility functions, and an interpretation of ``larger α is more fair''. We also construct an alternative set of axioms to capture system efficiency and feasibility constraints.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Tian Li
- Virginia Smith
- Maziar Sanjabi
- Ameet Talwalkar
- Aryan Mokhtari
- Shusen Wang
- Luping Wang
- Qiang Yang
In Electrical and Electronic Engineering
Citations in the corpus
- Fair Resource Allocation in Federated Learning (2019, background): “Among them, a unified framework is captured through α-fairness (Lan et al., 2010; Mo & Walrand, 2000), in which the emphasis on fairness can be tuned by changing a single parameter, α.”
- On the Efficiency-Fairness Trade-off (2012, unknown): “Recent work by Lan et al. (2010) has been devoted to theoretically characterizing Copyright: INFORMS holds copyright to this Articles in Advance version, which is made available to subscribers.”