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arXiv 1602.00499math.PR
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随机环境中无限服务器系统的扩展极限

Scaling limits for infinite-server systems in a random environment

Mariska Heemskerk, Johan van Leeuwaarden, Michel Mandjes

摘要:

本文研究了过分散到达过程对无限服务台系统性能的影响。在我们的设置中,通过每隔$Δ$时间单位从给定分布中提取到达率$Λ$来建模随机环境,从而产生i.i.d.到达率序列$Λ_1,Λ_2, \ldots$。应用鞅中心极限定理,得到了缩放队列长度过程的泛函中心极限定理.我们进行大偏差和推导的队列长度的尾部概率的对数渐近。事实证明,在快速变化的环境中(即,$Δ$相对于$Λ$是小的)到达过程的过度分散几乎不影响系统行为,而在缓慢变化的随机环境中,它是根本不同的;这一一般发现适用于中心极限和大偏差制度。我们将我们的结果扩展到每个到达在多个无限服务器队列中创建一个作业的设置。

英文摘要:

This paper studies the effect of an overdispersed arrival process on the performance of an infinite-server system. In our setup, a random environment is modeled by drawing an arrival rate $Λ$ from a given distribution every $Δ$ time units, yielding an i.i.d. sequence of arrival rates $Λ_1,Λ_2, \ldots$. Applying a martingale central limit theorem, we obtain a functional central limit theorem for the scaled queue length process. We proceed to large deviations and derive the logarithmic asymptotics of the queue length's tail probabilities. As it turns out, in a rapidly changing environment (i.e., $Δ$ is small relative to $Λ$) the overdispersion of the arrival process hardly affects system behavior, whereas in a slowly changing random environment it is fundamentally different; this general finding applies to both the central limit and the large deviations regime. We extend our results to the setting where each arrival creates a job in multiple infinite-server queues.

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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