# Enabling Long-term Fairness in Dynamic Resource Allocation

arXiv: https://arxiv.org/abs/2208.05898 · HTML: https://mlsanity.com/paper/2208.05898/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2022.

## Built on by (most cited first, 15 of 15)

- [No-regret Algorithms for Fair Resource Allocation](https://mlsanity.com/paper/2303.06396/) (2023)
- [Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach](https://mlsanity.com/paper/2406.13201/) (2024)
- [Fair Resource Allocation in Virtualized O-RAN Platforms](https://mlsanity.com/paper/2402.11285/) (2024)
- [Time Fairness in Online Knapsack Problems](https://mlsanity.com/paper/2305.13293/) (2023)
- [Fair Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/2408.17313/) (2024)
- [Long-term Fairness For Real-time Decision Making: A Constrained Online Optimization Approach](https://mlsanity.com/paper/2401.02552/) (2024)
- [Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System](https://mlsanity.com/paper/2402.09729/) (2024)
- [Pursuing Overall Welfare in Federated Learning through Sequential Decision Making](https://mlsanity.com/paper/2405.20821/) (2024)
- [α-Fair Contextual Bandits](https://mlsanity.com/paper/2310.14164/) (2023)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)
- [Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction](https://mlsanity.com/paper/2506.19890/) (2025)
- [Multi-Objective textitmin-max Online Convex Optimization](https://mlsanity.com/paper/2510.13560/) (2025)
- [Combinatorial Allocation Bandits with Nonlinear Arm Utility](https://mlsanity.com/paper/2603.07005/) (2026)
- [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026)
- [Price of Fairness in Short-Term and Long-Term Algorithmic Selections](https://mlsanity.com/paper/2605.06227/) (2026)

## Abstract (arXiv)

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.