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Time-stepped hybrid simulation (TSHS) for large scale networks

机译:大型网络的时步混合仿真(TSHS)

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Data communication networks have been experiencing tremendous growth in size, complexity, and heterogeneity over the last decade. This trend poses a significant challenge to the design of scalable performance evaluation methodologies. In this paper we propose time-stepped hybrid simulation (TSHS) to deal with the scalability issue faced by traditional packet-level discrete event simulation methods. TSHS is a framework that offers the user the flexibility to choose the simulation time scale so as to trade off the computational cost of the simulation with its fidelity. Simulation speedup is achieved by evaluating the system at coarser time-scales. The potential loss of simulation accuracy when fine-time-scale behavior is evaluated at a coarser time-scale is studied both analytically and experimentally.
机译:在过去的十年中,数据通信网络的规模,复杂性和异构性都经历了巨大的增长。这种趋势对可扩展性能评估方法的设计提出了重大挑战。在本文中,我们提出了时步混合仿真(TSHS),以解决传统的数据包级离散事件仿真方法所面临的可伸缩性问题。 TSHS是一个框架,可为用户提供选择仿真时间尺度的灵活性,从而以其保真度来权衡仿真的计算成本。通过在较粗的时间尺度上评估系统,可以实现仿真加速。通过分析和实验研究了在较粗略的时间尺度上评估精细时间尺度行为时仿真精度的潜在损失。

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