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On fast simulation techniques for queueing systems.

机译:关于排队系统的快速仿真技术。

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Communication networks have experienced dramatic growth in all dimensions: size, speed and heterogeneity etc. This poses great challenges to network modeling and performance evaluation. Various schemes have been trades off simulation fidelity for speedup. In this dissertation, we investigate simulation by utilizing special properties of queueing systems.; First we describe low and high resolution models of a simple queue via Poisson Driven Stochastic Differential Equations. Explicit formulas of evaluation errors are obtained, by which we identify the impacts of different traffic components and the utilization of the queue.; It is not new to simulate networks at a burst scale. For example, a cluster of closely spaced packets are modeled as a fluid chunk with a constant rate. In previous studies, the loss of accuracy is mainly assessed by experiments. There are fewer researches on quantitative characterization of simulation errors. We obtain error formulas for a specific queueing system. By that, we identify the occurrence of queue empty periods as a major contributor to the degradation of accuracy. This provides further understanding of the impacts of source traffic and queueing systems on the error.; Time stepped simulation (TSS) has been proposed to deal with the scalability issue encountered by event-driven fluid simulation and packet-level discrete event simulation. The systematic investigation on simulation errors is rather modest. We study the impacts of traffic short-term and long-term burstiness on the errors and show that the accuracy of TSS is related to traffic properties and system loads. In order to obtain tolerable degradation for a wider range of utilizations, we propose compensated TSS (CTSS). We also discuss the effect of traffic long-term burstiness and system load on the accuracy of TSS in capturing queue outputs, and briefly study TCP networks.; Motivated by simulation speedup, we explore decomposition phenomena in queueing systems. The original queue is converted to a new system, where a fast simulation can be applied. We prove the equivalence of the mean queue length of the two systems under certain circumstances.
机译:通信网络在各个方面都经历了戏剧性的增长:规模,速度和异构性等。这对网络建模和性能评估提出了巨大挑战。为了加速,已经折衷了各种方案来模拟仿真保真度。在本文中,我们利用排队系统的特殊特性研究了仿真。首先,我们通过泊松驱动随机微分方程描述简单队列的低分辨率和高分辨率模型。获得了明确的评估错误公式,通过这些公式,我们可以确定不同流量组件和队列利用率的影响。以突发规模模拟网络并不是什么新鲜事。例如,将紧密间隔的数据包簇建模为具有恒定速率的流体块。在以前的研究中,准确性的下降主要是通过实验来评估的。关于模拟误差的定量表征的研究较少。我们获得特定排队系统的错误公式。这样一来,我们确定队列空缺时间段的出现是导致准确性下降的主要因素。这样可以进一步了解源流量和排队系统对错误的影响。已提出时间步进仿真(TSS),以处理事件驱动的流体仿真和数据包级离散事件仿真遇到的可伸缩性问题。对模拟错误的系统研究相当有限。我们研究了流量短期和长期突发性对错误的影响,并表明TSS的准确性与流量属性和系统负载有关。为了获得更广泛使用范围的可容忍降级,我们提出了补偿TSS(CTSS)。我们还讨论了流量长期突发性和系统负载对TSS在捕获队列输出中的准确性的影响,并简要研究了TCP网络。受仿真加速的启发,我们探索了排队系统中的分解现象。原始队列将转换为新系统,可以在其中应用快速仿真。我们证明了在某些情况下两个系统的平均队列长度相等。

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