非货币公平调度-一种合作博弈方法
Non-monetary fair scheduling---a cooperative game theory approach
摘要：
我们考虑一个多组织的系统中，每个组织贡献的处理器的全球池，但也要处理的共同资源上的工作。调度算法的公平性对于系统的稳定性甚至存在都是至关重要的（因为组织可能会拒绝加入不公平的系统）。我们考虑在线，非透视顺序作业调度。已启动的作业不能停止、取消、抢占或移动到其他处理器。我们认为相同的处理器，但我们的大多数结果可以扩展到相关或不相关的处理器。我们将公平调度问题建模为合作博弈，并使用Shapley值来确定理想的公平调度。与现有文献不同的是，我们没有用金钱来评估工作的相对效用。相反，为了计算一个组织的贡献，我们确定这个组织的存在如何影响其他组织的绩效。我们的方法可以用于任意效用函数（例如，流时间，迟到，资源利用率），但我们认为，效用函数应该是战略弹性。应劝阻各组织不要拆分、合并或拖延其工作。我们提出了唯一的（在一个乘法和加法常数）策略弹性效用函数。我们表明，公平调度问题是NP难的，很难近似。然而，对于单位大小的工作，我们提出了一个FPRAS。此外，我们表明，与组织的数量参数化的问题是FPT。虽然对于大量的组织的问题是计算困难的，所提出的指数算法可以用作公平性基准。
英文摘要：
We consider a multi-organizational system in which each organization contributes processors to the global pool but also jobs to be processed on the common resources. The fairness of the scheduling algorithm is essential for the stability and even for the existence of such systems (as organizations may refuse to join an unfair system). We consider on-line, non-clairvoyant scheduling of sequential jobs. The started jobs cannot be stopped, canceled, preempted, or moved to other processors. We consider identical processors, but most of our results can be extended to related or unrelated processors. We model the fair scheduling problem as a cooperative game and we use the Shapley value to determine the ideal fair schedule. In contrast to the current literature, we do not use money to assess the relative utilities of jobs. Instead, to calculate the contribution of an organization, we determine how the presence of this organization influences the performance of other organizations. Our approach can be used with arbitrary utility function (e.g., flow time, tardiness, resource utilization), but we argue that the utility function should be strategy resilient. The organizations should be discouraged from splitting, merging or delaying their jobs. We present the unique (to within a multiplicative and additive constants) strategy resilient utility function. We show that the problem of fair scheduling is NP-hard and hard to approximate. However, for unit-size jobs, we present an FPRAS. Also, we show that the problem parametrized with the number of organizations is FPT. Although for the large number of the organizations the problem is computationally hard, the presented exponential algorithm can be used as a fairness benchmark.
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。
