发表机构
IIT Madras（印度马德拉斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究随机截止日期调度中的RMAB问题，基于惠特尔指数策略开发结果公平的惠特尔指数策略，定义虚拟队列机制，通过数值示例展示算法性能，比较不同策略，发现结果公平策略能提供更好公平性，还展示了公平与利润的权衡及随服务器容量的变化。
AI 中文摘要
我们研究了随机截止日期调度应用中的不安分多臂老虎机（RMAB）问题。RMAB问题通过惠特尔指数策略解决，目标是最大化预期累积折扣奖励。但惠特尔指数策略虽能最大化奖励，却在两类中不公平。本文引入公平标准，研究了RMAB的结果公平模型，将结果公平的随机截止日期调度问题表述为RMAB并开发了结果公平的惠特尔指数策略，定义了虚拟队列机制以动态保证不同群体的长期完成率。分析了标准惠特尔指数策略和结果公平指数策略，通过数值示例展示算法性能，比较了无公平性的惠特尔指数策略、输入公平的惠特尔指数策略和结果公平的惠特尔指数策略，发现结果公平的惠特尔指数策略在类间提供了更好的公平性，还展示了公平性与利润之间的权衡，且随着服务器容量增加这种权衡会减小。
英文摘要
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.
Comments8 pages, 4 figures, conference
