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Impact of workload model on PACS performance prediction

机译:工作量模型对PACS性能预测的影响

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Abstract: Discrete event simulation is often used to predict the performance of PACS systems. Although detailed models can be developed for the system elements, the image generation workload assumptions can have a significant impact on the results. The selection of a source model is a crucial part of any simulation and should be representative of the real application. If the source model is improperly selected it is possible to reach misleading conclusions. Examples of source models that have been used for the image workload in PACS include Poisson, modulated Poisson, and bursty. In this paper we compare the use of different image generation models and the impact on system simulation results. The BONeS simulation package is used to define a PACS system consisting of medical imaging device sources and destinations (archives and displays) which exchange images over a network consisting of ethernet and token bus subnetworks. All parameters ar identically specified for each simulation experiment with the only difference being the type of traffic generator used. The results illustrate that use of a Poisson model results in an overly optimistic estimate of system behavior. An alternative model is proposed which defines imaging device traffic patterns in terms of bursts of images within `studies.' The parameter sensitivity of this model is also examined. !14
机译:摘要:离散事件模拟通常用于预测PACS系统的性能。尽管可以为系统元素开发详细的模型,但是图像生成工作负载假设可能会对结果产生重大影响。源模型的选择是任何仿真的关键部分,应代表实际应用。如果源模型选择不当,可能会得出令人误解的结论。在PACS中已用于图像工作负载的源模型的示例包括泊松,调制泊松和猝发。在本文中,我们比较了不同图像生成模型的使用以及对系统仿真结果的影响。 BONeS仿真程序包用于定义由医学成像设备源和目的地(档案和显示器)组成的PACS系统,这些源和目的地通过由以太网和令牌总线子网组成的网络交换图像。为每个模拟实验均指定了所有参数,唯一的区别是所使用的流量生成器的类型。结果表明,使用泊松模型会导致对系统行为的过于乐观的估计。提出了一种替代模型,该模型根据“研究”中的图像突发来定义成像设备流量模式。还检查了该模型的参数敏感性。 !14

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