Price of fairness for opportunistic and priority schedulers

Malhar Mehta, V. Kavitha, N. Hemachandra
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引用次数: 2

Abstract

When agents compete for common resource and when the utilities derived by them, upon allocation, are independent across the agents and time slots, an opportunistic scheduler is used. The instantaneous utility of one agent can be low, however few among many would have `good' utility with high probability. Opportunistic schedulers utilize these opportunities, allocate resource at any time to a `good' agent. Efficient schedulers maximize the sum of accumulated utilities. Thus, every time `best' agent is allocated. This can result in negligible (unfair) accumulations for some agents, whose instantaneous utilities are `low' with high probability. Fair opportunistic schedulers are thus introduced (e.g., alpha-fair schedulers). We study their price of fairness (PoF). We group the agents into finite classes, each class having identical utilities and QoS requirements. We study the asymptotic PoF as agents increase, while maintaining class-wise proportions constant. Asymptotic PoF is less than one, depends only upon the differences in the largest utilities of individual classes and is less than the maximum such normalized differences. The PoF is zero initially and increases with increase in fairness requirements to an upper bound strictly less than one. We observe that the fair schedulers are essentially priority schedulers, which facilitated easy analysis of PoF.
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机会和优先级调度器的公平代价
当代理竞争公共资源时,当它们在分配时派生的实用程序在代理和时间段之间是独立的时,就会使用机会调度程序。一个代理的瞬时效用可能很低,但在许多代理中很少有高概率具有“良好”效用。机会调度程序利用这些机会,随时将资源分配给“好的”代理。高效的调度器使累积的实用程序的总和最大化。因此,每次分配“最佳”代理。这可能导致一些代理的累积可以忽略不计(不公平),它们的瞬时效用很可能是“低”的。因此引入了公平机会调度器(例如,alpha-公平调度器)。我们研究了它们的公平价格(PoF)。我们将代理分成有限的类,每个类具有相同的实用程序和QoS需求。我们研究了智能体增加时的渐近PoF,同时保持类明智比例不变。渐近PoF小于1,仅取决于单个类的最大效用的差异,并且小于这种归一化差异的最大值。PoF最初为零,随着公平性要求的增加而增加,直到一个严格小于1的上界。我们观察到公平调度程序本质上是优先级调度程序,这便于分析PoF。
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