A Flow-Level Wi-Fi Model for Large Scale Network Simulation

Clément Courageux-Sudan, Loic Guegan, Anne-Cécile Orgerie, M. Quinson
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引用次数: 2

Abstract

Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.
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大规模网络仿真的流级Wi-Fi模型
Wi-Fi网络被广泛用于向终端用户提供互联网接入和在边缘部署应用程序。由于在现代网络中发挥着重要作用,Wi-Fi网络正变得越来越大、越来越密集。然而,在大规模和可复制的方式下研究它们的性能仍然是一项具有挑战性的任务。目前的解决方案包括真实的实验和模拟。虽然实验的规模受到其财务成本和商业网络潜在干扰的限制,但由于其模型的粒度和计算运行时间,模拟也缺乏可扩展性。本文介绍了一种新的用于大规模仿真的Wi-Fi模型。该模型基于流级模拟,与最先进的模型相比,在估算无线介质上的带宽共享时需要更少的计算,从而具有更好的可伸缩性。将我们的模型与已经存在的ns-3的Wi-Fi实现进行比较,我们表明我们的方法产生了接近的性能评估,同时将模拟的运行时间提高了几个数量级。使用这种模型可以使研究人员比以前更快地获得由数千个节点组成的网络的可重复结果。
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