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Efficiency of time segmentation parallel simulation of finite Markovian queueing networks

机译:有限马尔可夫排队网络的时间分割并行仿真效率

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We present an approach for using parallel processors to simulate long sample paths of Markovian queueing networks with finite buffers and both loss and blocking stations. Parallelism is achieved by distributing the available processors among segments of the time domain of the simulation. We conduct the simulation in such a way that all sample paths of the system will eventually couple (i.e., become identical), regardless of their starting states. This coupling property is exploited to generate valid sample paths of the system by combining the information collected on consecutive time segments of the simulation. The efficiency of our approach depends heavily on the magnitude of the coupling times of the sample paths. We study how the expected coupling times depend on the system parameters through a variety of theoretical and numerical results. Our main results give conditions under which the expected coupling times grow slowly (no faster than linearly) with respect to the number of stations and buffer capacities in the system.: These results suggest that our time segmentation approach is likely to perform well on a substantial class of finite Markovian queueing networks. [References: 15]
机译:我们提出了一种使用并行处理器来模拟具有有限缓冲区以及丢失站和阻塞站的马尔可夫排队网络的长采样路径的方法。并行性是通过在仿真时域的各个段之间分配可用处理器来实现的。我们以这样一种方式进行仿真,即系统的所有采样路径最终都将耦合(即变得相同),而不管它们的起始状态如何。通过结合在模拟的连续时间段上收集的信息,利用该耦合属性来生成系统的有效样本路径。我们方法的效率在很大程度上取决于采样路径耦合时间的大小。我们通过各种理论和数值结果来研究预期的耦合时间如何取决于系统参数。我们的主要结果给出了这样的条件:相对于系统中的站数和缓冲区容量,预期的耦合时间增长缓慢(不快于线性增长):这些结果表明,我们的时间分段方法可能在相当长的时间内表现良好有限马尔可夫排队网络。 [参考:15]

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