提出公平性约束的随机截止时间调度模型，提升弱势群体服务公平性。
Outcome-Fair Restless Multi-Armed Bandits for Stochastic Deadline Scheduling
- 引入基于虚拟队列的动态保障机制，实现跨群体长期完成率公平
- 在服务器容量较高时，公平性与收益的权衡更优，性能优于传统策略
- 适用于需兼顾效率与社会公平的资源调度场景，如医疗、教育系统
我们研究了一种用于随机截止时间调度的应用的随机非平稳多臂赌博机（RMAB）问题。传统Whittle索引策略虽能最大化累积折扣奖励，但对两类用户缺乏公平性。本文引入公平性准则，构建了面向结构性弱势群体的成果公平型RMAB模型，并提出成果公平的Whittle索引策略。通过设计虚拟队列机制，动态保障不同人口群体的长期任务完成率。分析标准Whittle策略与成果公平策略的性能表现，数值实验对比了三种策略：无公平性Whittle策略、输入公平性策略、成果公平策略。结果表明，成果公平策略显著提升群体间公平性，且在服务器容量增大时，公平性与收益的权衡更优。
原文摘要 · Abstract (English)
We study a restless multi-armed bandit (RMAB) problem for a stochastic deadline scheduling application. RMAB problems are solved using the Whittle index policy. The goal in RMAB is to maximize the expected cumulative discounted reward maximization. The Whittle index policy maximizes reward, but is not fair among two classes. In this paper, we introduce fairness criteria and study an outcome-fair model for RMAB which allows fairness for jobs and users structurally disadvantaged demographic classes. We formulate an outcome fair stochastic deadline scheduling problem as RMAB, and we develop the outcome fair Whittle index policy. We define a virtual queue mechanism that dynamically enforces long-term completion rate guaranties across demographic groups. We analyze a standard Whittle index policy and the outcome-fair index policy. We demonstrate the performance of our algorithms with numerical examples. We compare policies---Whittle index policy (no fairness), input-fairness Whittle index policy, outcome fair Whittle index policy. We observe that the outcome-fair Whittle index policy provides better fairness among classes compared to other policies. We demonstrate a trade off between fairness and profit. This decreases as the server capacity increases.
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