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arXiv 2003.03655math.PR
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具有重尾处理时间分布的最短剩余处理时间队列的高流量标度极限

Heavy Traffic Scaling Limits for shortest remaining processing time queues with heavy tailed processing time distributions

Sayan Banerjee, Amarjit Budhiraja, Amber L. Puha

AI总结:

该研究针对具有有限二阶矩、正则变化重尾处理时间分布的SRPT单服务器队列,证明非标准标度下测度值过程依分布收敛且无状态空间坍缩,还建立了工作量与队列长度过程的收敛性及尾部变轻时的坍缩趋近特性。

摘要:

我们研究了在最短剩余处理时间(SRPT,shortest remaining processing time)调度策略下运行的单服务器队列;即服务器采用抢占式方式,优先服务剩余处理时间最短的作业。在本研究中,我们旨在研究描述一系列SRPT排队系统演化的、经过适当标度的测度值状态描述符的渐近行为。Gromoll、Kruk与Puha(2011)已在扩散标度下研究了该问题,在处理时间分布具有无界支撑的设定下,他们在合适条件下证明了扩散标度后的测度依分布收敛于恒为零的过程。Puha(2015)针对处理时间分布具有无界支撑且为轻尾的场景,证明了队列长度过程的一种非标准标度会引发某种形式的状态空间坍缩,最终得到非零极限。在本研究中,我们考虑处理时间分布具有有限二阶矩且尾部正则变化的情形。我们证明,经过非标准标度的测度值过程,在测度路径空间中依分布收敛。与先前结果形成鲜明对比的是,本情形下不存在状态空间坍缩。尽管如此,极限的描述形式十分简洁,可通过由单个布朗运动确定的某一$\boldsymbol{\rm R}_+$值随机场明确给出。在研究过程中,我们还建立了经过适当标度的工作量过程与队列长度过程的收敛性。我们同时证明,当作业处理时间分布的尾部以适当方式变轻时,极限队列长度过程与极限工作量过程的差值收敛于零,从而趋近于状态空间坍缩的行为。

英文摘要:

We study a single server queue operating under the shortest remaining processing time (SRPT) scheduling policy; that is, the server preemptively serves the job with the shortest remaining processing time first. In this work we are interested in studying the asymptotic behavior of suitably scaled measure-valued state descriptors that describe the evolution of a sequence of SRPT queuing systems. Gromoll, Kruk, and Puha (2011) have studied this problem under diffusive scaling. In the setting where the processing time distributions have unbounded support, under suitable conditions, they show that the diffusion scaled measures converge in distribution to the process that is identically zero. In Puha (2015) for the setting where the processing time distributions have unbounded support and light tails, a non-standard scaling of the queue length process is shown to give rise to a form of state space collapse that results in a nonzero limit. In the current work we consider the case where processing time distributions have finite second moments and regularly varying tails. We show that the measure valued process, under a non-standard scaling, converges in distribution in the space of paths of measures. In sharp contrast with previous results, there is no state space collapse. Nevertheless, the description of the limit is simple and given explicitly in terms of a certain $\mathbb{R}_+$ valued random field which is determined from a single Brownian motion. Along the way we establish convergence of suitably scaled workload and queue length processes. We also show that as the tail of the distribution of job processing times becomes lighter in an appropriate fashion, the difference between the limiting queue length process and the limiting workload process converges to zero, thereby approaching the behavior of state space collapse.

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