云计算中基于自适应速度公平的策略性多资源分配
The Price of Strategyproofness in Fair Multi-Resource Allocation for Cloud Computing
浏览论文内容
中文总结 AI 辅助
针对云计算中多资源公平分配问题，提出自适应速度公平（ASF）框架，在满足共享激励、无嫉妒和帕累托最优的同时，实现策略性，并显著提升社会福利公平比率至约1.09384，优于现有机制。
中文摘要 AI 辅助
我们研究具有Leontief效用的多个可分资源的公平且策略性分配，其动机来自云计算。经典机制——主导资源公平（DRF）——满足共享激励（SI）、无嫉妒性（EF）、策略性（SP）和帕累托最优（PO），但在功利主义社会福利方面可能效率极低。在经典近似基准下，没有任何机制能在满足SI、EF和SP中任意一个的同时，改善平凡的最坏情况保证。因此，我们采用最近引入的公平比率基准，该基准仅将机制与本身满足SI和EF的福利最大化分配进行比较。对于两种资源，我们引入了自适应速度公平（ASF），这是一个统一的参数化框架，将之前的机制作为特例或边界情况。每个ASF机制都满足SI、EF和PO，并且我们推导出一个保证SP的通用充分条件。在此框架内进行优化，得到一个策略性机制，其渐近公平比率为$2/(2\sqrt{2}-1)\approx1.09384$，显著改善了之前的最佳保证$3-\sqrt{3}\approx1.268$。我们用所有ASF机制的下界$1.07894$来补充这个上界，表明我们的最佳机制在此框架内接近最优。在合成和Google trace生成的实例上的实验支持了理论，并展示了强大的实证性能。最后，我们建立了一个尖锐的维度边界。对于具有$m\ge3$个资源的通用设置，每个满足SI和SP的机制都有精确的公平比率$m$。对于满足事后SI和期望真实性的随机机制，相同的因子$m$下界继续成立。
英文摘要
We study fair and strategy-proof allocation of multiple divisible resources with Leontief utilities, motivated by cloud computing. The canonical mechanism, Dominant Resource Fairness (DRF), satisfies sharing incentive (SI), envy-freeness (EF), strategy-proofness (SP), and Pareto optimality (PO), but can be highly inefficient in terms of utilitarian social welfare. Under the classical approximation benchmark, no mechanism satisfying even one of SI, EF, and SP can improve on the trivial worst-case guarantee. We therefore adopt the recently introduced \emph{fair-ratio} benchmark, which compares a mechanism only with the welfare-maximizing allocation that itself satisfies SI and EF. For two resources, we first introduce Adaptive-Speed Fairness (ASF), a unified parametric framework that captures previous mechanisms as special or boundary cases. Every ASF mechanism satisfies SI, EF, and PO, and we derive a general sufficient condition that guarantees SP. Optimizing within this framework yields a strategy-proof mechanism with asymptotic fair-ratio $2/(2\sqrt{2}-1)\approx 1.094$, substantially improving the previous best guarantee $3-\sqrt{3}\approx1.268$. We complement this upper bound with a lower bound of $1.0789$ for all ASF mechanisms. To overcome this limitation, we introduce Corrected Resource Balancing (CRB), which uses the full resource-load structure together with a one-agent incentive correction. CRB satisfies SI, EF, SP, and PO and achieves fair-ratio at most $1+1/n$. Together with a general $1+Ω(1/n)$ lower bound, this establishes the optimal asymptotic order $1+Θ(1/n)$ for two resources. Finally, for every $m\ge3$, any deterministic mechanism satisfying SI and SP has fair-ratio exactly $m$. The same lower bound holds for randomized mechanisms satisfying ex-post SI and truthfulness in expectation.
发表机构
- Beijing Jiaotong University（北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。
