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On Network Model Division Method Based on Link-to-Link Traffic Intensity for Accelarating Parallel Simulatioin

机译:基于链路到链路流量强度的网络模型划分方法用于加速并行仿真

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摘要

In recent years, requirements for performance evaluation techniques of a large-scale network have been increasing. However, the conventional network performance evaluation techniques, such as mathematical modeling and simulation, are suitable for comparatively small-scale networks. Research on parallel simulation has been actively done in recent years, which might be a possible solution for simulating a large-scale network. However, since most existing network simulators are event-driven, parallelization of a network simulator is not easy task. In this paper, a novel network model division method based on link-to-link traffic intensity for accelerating parallel simulator of a large-scale network is proposed. The key ideas of our network model division method are as follows: (1) perform steady state analysis for the network model that is to be simulated, and estimate all traffic intensities along links in steady state, (2) repeatedly apply the minimum cut algorithm from graph theory based on the estimated traffic intensities, so that the simulation model is divided at the link that has little traffic intensities in steady state.
机译:近年来,对大型网络的性能评估技术的需求已经增加。但是,常规的网络性能评估技术(例如数学建模和仿真)适用于规模相对较小的网络。近年来,关于并行仿真的研究已经积极开展,这可能是仿真大型网络的一种可能的解决方案。但是,由于大多数现有的网络模拟器都是事件驱动的,因此网络模拟器的并行化并非易事。提出了一种基于链路间通信强度的网络模型划分新方法,用于加速大规模网络的并行仿真器。我们的网络模型划分方法的关键思想如下:(1)对要模拟的网络模型进行稳态分析,并估计稳态下沿链路的所有流量强度;(2)重复应用最小割算法从图论出发,基于估计的交通强度,从而将仿真模型划分为稳态时交通强度较小的链接。

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