Multiscale fairness and its application to resource allocation in wireless networks

Altman, Eitan; Avrachenkov, Konstantin; Ramanath, Sreenath

doi:10.1016/j.comcom.2012.01.013

Cited by 15 publications

(14 citation statements)

References 16 publications

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“…Altman et al [4] consider the 𝛼-fairness problem in a dynamic resource allocation, and investigate fairness enforced at different time scales (instantaneous and long-term). They consider known utilities at the time of selecting an allocation in a stationary setting.…”

Section: Fairness In Dynamic Resource Allocationmentioning

confidence: 99%

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Enabling Long-term Fairness in Dynamic Resource Allocation

Si-Salem

Iosifidis

Neglia

2022

Proc. ACM Meas. Anal. Comput. Syst.

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We study the fairness of dynamic resource allocation problem under the α-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests.

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Section: Fairness In Dynamic Resource Allocationmentioning

confidence: 99%

“…, 𝐹 }. 4 We model a cache network at timeslot 𝑡 ∈ T as an undirected weighted graph 𝐺 𝑡 (C, E), where C ≜ {1, 2, . .…”

Section: Multi-agent Cache Networkmentioning

confidence: 99%

Enabling Long-term Fairness in Dynamic Resource Allocation

Si-Salem

Iosifidis

Neglia

2022

Proc. ACM Meas. Anal. Comput. Syst.

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“…The signaling delays make the timescale separation assumption unrealistic. Generally, there have been two categories of studies that have examined the removal of the timescale separation assumption: (1) studies that use intermediate iterates as decisions and assume continuous underlying flows [47,48], and (2) studies that use a multi-timescale approach across different layers of the protocol stack [49,50]. More specifically, the study [47] showed that a β-fairness utility function can be maximized, while guaranteeing system stability, under the assumptions that the number of users per class follows a recurrent Markov Chain.…”

Section: Network Optimizationmentioning

confidence: 99%

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

et al. 2019

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With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

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“…As in [3], we have modified the α-fairness to remove discontinuity without affecting the optimal solution. Note also that optimization of the discounted sum of the α-fairness results in better instantaneous fairness [4]. Thus time-discounting is well suited to avoid imbalances on shorter time scales.…”

Section: Problem Formulationmentioning

confidence: 99%

Controlling G-AIMD by index policy

Avrachenkov

Borkar

Pattathil

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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We consider the Generalized Additive Increase Multiplicative Decrease (G-AIMD) dynamics for resource allocation with alpha fairness utility function. This dynamics has a number of important applications such as internet congestion control, charging electric vehicles, and smart grids. We prove indexability for the special case of MIMD model and provide an efficient scheme to compute the index. The use of index policy allows us to avoid the curse of dimensionality. We also demonstrate through simulations for another special case, AIMD, that the index policy is close to optimal and significantly outperforms a natural heuristic which penalizes the strongest user.

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Multiscale fairness and its application to resource allocation in wireless networks

Cited by 15 publications

References 16 publications

Enabling Long-term Fairness in Dynamic Resource Allocation

Enabling Long-term Fairness in Dynamic Resource Allocation

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

Controlling G-AIMD by index policy

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