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arXiv 2006.06150math.PR
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Markov到达下交换机中的重流量队列长度行为

Heavy Traffic Queue Length Behaviour in a Switch under Markovian Arrivals

Shancong Mou, Siva Theja Maguluri

AI总结:

本文将漂移方法推广至Markov到达,精确刻画MaxWeight输入排队交换机的重流量缩放平均总队列长度,并给出全流量机制下渐近紧的上下界。

摘要:

本文研究当到达过程服从Markov过程时,在MaxWeight算法下运行的输入排队交换机。我们精确刻画了重流量极限下经重流量缩放的平均总队列长度,并表明它与一个通用下界之间的差距因子小于2。此外,我们获得了适用于所有流量机制的下界和上界,并且这些界在重流量机制下变得紧。本文通过将最近为独立同分布(i.i.d.)到达情形开发的漂移方法推广到Markov到达情形来获得这些结果。本文首先在Markov到达下的单服务器队列中获得重流量平均队列长度及其分布,然后将其应用于输入排队交换机情形,以此阐明这种推广。关键思想是利用有限状态Markov链的几何混合特性,并选择一个时间范围,使混合导致的误差依赖于重流量参数。

英文摘要:

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 shows 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 they become tight in the heavy-traffic regime. The paper obtains these results by generalizing the drift method recently developed for the case of i.i.d. arrivals, to the case of Markovian arrivals. The paper illustrates 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 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 picked so that the error due to mixing depends on the heavy-traffic parameter.

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。

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