提出信用公平机制，让资源借贷者在后续获得优先补偿。
Credit Fairness: Online Fairness In Shared Resource Pools
- 基于信用公平设计新分配机制，早期贡献资源者后续优先回收
- 新机制实现帕累托效率且避免长期资源分配不均
- 适合动态共享资源场景，如云计算资源调度
我们研究在需求随时间变化、效用函数为有界线性函数的环境下，对多个参与者重复分配共享资源的问题。独立地在每一轮中最大化最小化归一化效用，能保证参与激励（参与者至少不排斥参与）、策略无关性（无谎报动机）和帕累托效率。然而该最大最小机制可能导致即使平均需求相同，不同参与者最终获得的总资源量存在显著差异。为此我们引入‘信用公平’这一性质：与帕累托效率结合时，使早期借出资源的参与者在后续轮次中优先收回资源。信用公平可分别与帕累托效率或策略无关性同时满足，但若要求匿名性，则无法同时满足两者。我们提出一种既满足信用公平又具帕累托效率的新机制，并在计算资源共享场景中进行了评估。
原文摘要 · Abstract (English)
We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities. In this setting, independently maximizing the minimum endowment-normalized utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency. However, this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. We introduce credit fairness, a property that, together with Pareto efficiency, strengthens sharing incentives by giving agents who lend resources in early rounds priority toward recouping those resources in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness individually, but we show that, under anonymity, it cannot be achieved together with both. We propose a mechanism that is credit fair and Pareto efficient, and evaluate it in a computational resource-sharing setting.
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