Differentiated bandwidth sharing with disparate flow sizes

G. V. Kessel, R. Nunez-queija, S. Borst
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引用次数: 31

Abstract

We consider a multi-class queueing system operating under the discriminatory processor-sharing (DPS) discipline. The DPS discipline provides a natural approach for modeling the flow-level performance of differentiated bandwidth-sharing mechanisms. Motivated by the extreme diversity in flow sizes observed in the Internet, we examine the system performance in an asymptotic regime where the flow dynamics of the various classes occur on separate time scales. Specifically, from the perspective of a given class, the arrival and service completions of some of the competing classes (called mice) evolve on an extremely fast time scale. In contrast, the flow dynamics of the remaining classes (referred to as elephants) occur on a comparatively slow time scale. Assuming a strict separation of time scales, we obtain simple explicit expressions for various performance measures of interest, such as the distribution of the numbers of flows, mean delays, and flow throughputs. In particular, the latter performance measures are insensitive, in the sense that they only depend on the service requirement distributions through their first moments. Numerical experiments show that the limiting results provide remarkably accurate approximations in certain cases.
具有不同流量大小的差异化带宽共享
考虑了一个基于歧视性处理器共享(DPS)原则的多类排队系统。DPS学科为差分带宽共享机制的流级性能建模提供了一种自然的方法。由于在互联网上观察到的流量大小的极端多样性,我们研究了系统在渐近状态下的性能,其中各种类别的流量动力学发生在不同的时间尺度上。具体来说,从给定类的角度来看,一些竞争类(称为老鼠)的到达和服务完成在极快的时间尺度上进化。相比之下,其他种类(被称为大象)的流动动力学发生在相对较慢的时间尺度上。假设严格分离时间尺度,我们获得了各种感兴趣的性能度量的简单显式表达式,例如流量数量的分布,平均延迟和流量吞吐量。特别是,后一种性能度量是不敏感的,因为它们只依赖于最初时刻的服务需求分布。数值实验表明,极限结果在某些情况下提供了非常精确的近似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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