变异性感知的网络效用最大化
Variability Aware Network Utility Maximization
AI总结：
本文提出一种广义网络效用最大化框架，显式纳入用户分配奖励的时间变异性负面影响，并设计在线算法在平稳遍历假设下实现渐近最优的均值-变异性权衡。
摘要：
网络效用最大化（NUM）为研究跨经济学、法律和工程等学科的用户/实体集合中的资源分配提供了关键概念框架。在网络工程中，该框架对于理解互联网协议如何分配带宽特别有洞察力，并激发了关于分布式机制的各种研究，以在考虑能源/功率、存储、稳定性等新相关约束的同时最大化网络效用，适用于从通信网络到智能电网的系统。然而，当可用资源和/或用户效用随时间变化时，用户的分配往往会变化，这可能对用户的效用或体验质量产生不利影响。本文引入了一个广义NUM框架，显式地考虑了用户分配奖励的时间变异性的负面影响。它明确地包含了用户分配中均值和变异性之间的权衡。我们提出了一种在线算法来实现方差敏感的NUM，在平稳遍历假设下，该算法被证明是渐近最优的，即实现了与知道系统未来变异性的离线算法相同的时间平均。这极大地将NUM的工作扩展到了一类有趣的相关问题，其中用户/实体对其服务或分配奖励的时间变异性敏感。
英文摘要：
Network Utility Maximization (NUM) provides the key conceptual framework to study resource allocation amongst a collection of users/entities across disciplines as diverse as economics, law and engineering. In network engineering, this framework has been particularly insightful towards understanding how Internet protocols allocate bandwidth, and motivated diverse research on distributed mechanisms to maximize network utility while incorporating new relevant constraints, on energy/power, storage, stability, etc., for systems ranging from communication networks to the smart-grid. However when the available resources and/or users' utilities vary over time, a user's allocations will tend to vary, which in turn may have a detrimental impact on the users' utility or quality of experience. This paper introduces a generalized NUM framework which explicitly incorporates the detrimental impact of temporal variability in a user's allocated rewards. It explicitly incorporates tradeoffs amongst the mean and variability in users' allocations. We propose an online algorithm to realize variance-sensitive NUM, which, under stationary ergodic assumptions, is shown to be asymptotically optimal, i.e., achieves a time-average equal to that of an offline algorithm with knowledge of the future variability in the system. This substantially extends work on NUM to an interesting class of relevant problems where users/entities are sensitive to temporal variability in their service or allocated rewards.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
