An Axiomatic Theory of Fairness in Network Resource Allocation

Lan, Tian; Kao, David T. H.; Chiang, Mung; Sabharwal, Ashutosh

doi:10.1109/infcom.2010.5461911

Cited by 343 publications

(345 citation statements)

References 20 publications

(46 reference statements)

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“…In particular, a higher value of the inequality aversion parameter is thought to correspond to a "fairer" scheme (see [18], [2], [12]). Note that for the smallest value of α = 0, we recover the utilitarian principle, which is neutral towards inequalities.…”

Section: Efficiency-maximizing and Fair Allocationsmentioning

confidence: 99%

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On the Efficiency-Fairness Trade-off

2012

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A central decision maker allocates resources to each of n players who derive utility from their allocation. Consequently, her actions may be viewed as choosing an allocation of utilities u = (u 1 , u 2 , . . . , u n ) from some set of feasible allocations U ∈ R n + . Consider a decision maker that chooses an allocation u ∈ U to maximizeThe above family of objective functions proposed originally by Atkinson and parameterized by α ∈ R + , permits the decision maker to tradeoff efficiency (by lowering α) for fairness (by increasing α). The family of Atkinson utility functions is canonical in that it captures the efficient or "utilitarian" allocation (α = 0), the "max-min" fair allocation (α → ∞), and the proprotionally fair (or Nash bargaining) allocation (α → 1).This paper characterizes the tradeoff between efficiency and fairness in this general setting. In particular, we demonstrate that under reasonable assumptions on U , the total utility to players under a fair allocation, FAIR(U, α), and the total utility to players under a perfectly efficient allocation, FAIR(U, 0) SYSTEM(U ), must satisfy, and moreover, that this bound is essentially tight.

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Section: Efficiency-maximizing and Fair Allocationsmentioning

confidence: 99%

“…Recent work has been devoted to theoretically characterizing what it actually means for a higher value of α to be more fair [12]. The impact of a higher value of α on the system efficiency has also received a lot of attention.…”

Section: Efficiency-maximizing and Fair Allocationsmentioning

confidence: 99%

On the Efficiency-Fairness Trade-off

2012

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“…Then for any α > 0, Z i (x i , s i , α) is strictly concave in s i . We conclude that Z(x i , s i , α) is strictly concave in s for any α > 0 and therefore there is a unique solution s * (α) to (2).…”

Section: Definition 1 An Assignment S Is α-Fair If It Is a Solution Ofmentioning

confidence: 72%

“…This, as well as other fairness notions can be defined through a set of axioms, see [2]. This paper is inspired by several papers which already observed or derived fairness at different time-scales [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multiscale fairness and its application to resource allocation in wireless networks

Altman

Avrachenkov

Ramanath

2012

Computer Communications

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Abstract. Fair resource allocation is usually studied in a static context, in which a fixed amount of resources is to be shared. In dynamic resource allocation one usually tries to assign resources instantaneously so that the average share of each user is split fairly. The exact definition of the average share may depend on the application, as different applications may require averaging over different time periods or time scales. Our main contribution is to introduce new refined definitions of fairness that take into account the time over which one averages the performance measures. We examine how the constraints on the averaging durations impact the amount of resources that each user gets.

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“…A commonly studied trade-off when considering different resource allocation optimization objectives is that between efficiency and fairness [3,24]. Moreover, a key question that arises is at which granularity level should fairness be quantified and measured [1].…”

Section: Notion Of Fairness In Shared Environmentsmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

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An Axiomatic Theory of Fairness in Network Resource Allocation

Cited by 343 publications

References 20 publications

On the Efficiency-Fairness Trade-off

On the Efficiency-Fairness Trade-off

Multiscale fairness and its application to resource allocation in wireless networks

A new QoE fairness index for QoE management

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