Enabling Long-term Fairness in Dynamic Resource Allocation (2208.05898v2)

Abstract: We study the fairness of dynamic resource allocation problem under the $\alpha$-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.

• The paper establishes horizon-fairness as a criterion that accumulates utilities over time with a lower social welfare cost than slot-fairness.
• The paper presents an online policy that ensures vanishing regret while managing resource allocation under realistic adversarial constraints.
• The study validates its framework through a virtualized caching system, demonstrating effective fairness and optimal resource distribution in practice.

The paper "Enabling Long-term Fairness in Dynamic Resource Allocation" explores the complexities of achieving fairness in dynamic resource allocation settings under the $\alpha$-fairness criterion. The paper differentiates between two pivotal fairness objectives:

1. Slot-fairness: This objective focuses on ensuring fairness at every individual timeslot. It is well-studied and aims to maintain an equitable distribution of resources in real-time.
2. Horizon-fairness: Less explored compared to slot-fairness, this objective is concerned with the fairness of utilities accumulated over an extended time horizon. The authors argue that horizon-fairness imposes lesser constraints on social welfare compared to slot-fairness.

The paper's primary contribution lies in its analysis and robustness of horizon-fairness as a fairness criterion using regret as the performance metric. The paper acknowledges that achieving vanishing regret, indicative of optimal long-term fairness, is infeasible when facing an unrestricted adversary. To navigate this challenge, the authors offer practical restrictions on adversary capabilities derived from realistic operational scenarios.

The paper introduces an online policy designed to ensure vanishing regret within these constrained adversarial contexts. In essence, the policy promotes a balance between fairness and strategic flexibility, effectively managing to uphold fairness across a time horizon while maintaining competitive social welfare.

To validate the proposed fairness framework, the authors apply it to a virtualized caching system scenario. This system involves cooperative caches that satisfy content requests, demonstrating the framework's practical applicability and effectiveness in a real-world resource management problem.

Overall, the paper makes significant strides in bridging theoretical constructs of fairness with practical implementations in dynamic resource allocation, emphasizing the lower cost in terms of social welfare associated with horizon-fairness and presenting actionable strategies to achieve it.