Fair Allocation of Heterogeneous and InterchangeableResources
Fair Allocation of Heterogeneous and InterchangeableResources
复制标题
异构和可互换资源的公平分配
DOI：
10.1145/3305218.3305227
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Zhenhua Liu
中科院分区：
文献类型：
--
作者：
Xiao Sun;T. Le;Mosharaf Chowdhury;Zhenhua Liu
在异构处理器如多核cpu、gpu、tpu和其他用于机器学习的加速器的激增的激励下，我们提出了一个新的多可互换资源分配（MIRA）问题，其中一些资源是可互换的。挑战在于如何在共享系统中为用户分配可互换的资源，同时保持共享激励、帕累托效率和免嫉妒等理想属性。在本文中，我们首先证明了现有的算法，包括生产系统中使用的主导资源公平，不能为可互换资源提供这些属性。然后，我们描述了性能和策略证明之间的权衡，并设计了基于预算的（BUD）算法，该算法保留了帕累托效率，共享激励和嫉妒性，同时提供了比当前使用的算法更好的性能。
Motivated by the proliferation of heterogeneous processors such as multi-core CPUs, GPUs, TPUs, and other accelerators for machine learning, we formulate a novel multiinterchangeable resource allocation (MIRA) problem where some resources are interchangeable. The challenge is how to allocate interchangeable resources to users in a sharing system while maintaining desirable properties such as sharing incentive, Pareto efficiency, and envy-freeness. In this paper, we first show that existing algorithms, including the Dominant Resource Fairness used in production systems, fail to provide these properties for interchangeable resources. Then we characterize the tradeoff between performance and strategyproofness, and design the Budget-based (BUD) algorithm, which preserves Pareto efficiency, sharing incentive and envyfreeness while providing better performance over currently used algorithms.