Heavy-traffic queue length behavior in a switch under Markovian arrivals

Shancong Mou, S. T. Maguluri
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Abstract

This paper studies the input-queued switch operating under the MaxWeight algorithm when the arrivals are according to a Markovian process. We exactly characterize the heavy-traffic scaled mean sum queue length in the heavy-traffic limit, and show that it is within a factor of less than 2 from a universal lower bound. Moreover, we obtain lower and upper bounds that are applicable in all traffic regimes and become tight in the heavy-traffic regime. We obtain these results by generalizing the drift method recently developed for the case of independent and identically distributed arrivals to the case of Markovian arrivals. We illustrate this generalization by first obtaining the heavy-traffic mean queue length and its distribution in a single-server queue under Markovian arrivals and then applying it to the case of an input-queued switch. The key idea is to exploit the geometric mixing of finite-state Markov chains, and to work with a time horizon that is chosen so that the error due to mixing depends on the heavy-traffic parameter.
马尔可夫到达条件下交换机中的大流量队列长度行为
本文研究了根据马尔可夫过程到达的输入队列交换机在 MaxWeight 算法下的运行情况。我们精确描述了大流量限制下的大流量标度平均队列长度总和,并证明它与通用下限的误差小于 2 倍。此外,我们还获得了适用于所有交通状况的下界和上界,并在大交通状况下变得非常严格。我们将最近针对独立且同分布到达情况开发的漂移方法推广到马尔可夫到达情况,从而获得了这些结果。我们首先求出马尔可夫到达情况下的大流量平均队列长度及其在单服务器队列中的分布,然后将其应用于输入队列交换机的情况,以此说明这种推广方法。关键的思路是利用有限状态马尔可夫链的几何混合,并选择一个时间跨度,使混合造成的误差取决于重流量参数。
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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