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On the Validity of Flow-level TCP Network Models for Grid and Cloud Simulations

机译:流级TCP网络模型在网格和云计算中的有效性

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Researchers in the area of grid/cloud computing perform many of their experiments using simulations that must capture network behavior. In this context, packet-level simulations, which are widely used to study network protocols, are too costly given the typical large scales of simulated systems and applications. An alternative is to implement network simulations with less costly flow-level models. Several flow-level models have been proposed and implemented in grid/cloud simulators. Surprisingly, published validations of these models, if any, consist of verifications for only a few simple cases. Consequently, even when they have been used to obtain published results, the ability of these simulators to produce scientifically meaningful results is in doubt. This work evaluates these state-of-the-art flow-level network models of TCP communication via comparison to packet-level simulation. While it is straightforward to show cases in which previously proposed models lead to good results, instead we follow the critical method, which places model refutation at the center of the scientific activity, and we systematically seek cases that lead to invalid results. Careful analysis of these cases reveals fundamental flaws and also suggests improvements. One contribution of this work is that these improvements lead to a new model that, while far from being perfect, improves upon all previously proposed models in the context of simulation of grids or clouds. A more important contribution, perhaps, is provided by the pitfalls and unexpected behaviors encountered in this work, leading to a number of enlightening lessons. In particular, this work shows that model validation cannot be achieved solely by exhibiting (possibly many) "good cases." Confidence in the quality of a model can only be strengthened through an invalidation approach that attempts to prove the model wrong.
机译:网格/云计算领域的研究人员使用必须捕获网络行为的模拟来执行许多实验。在这种情况下,鉴于典型的大规模仿真系统和应用,广泛用于研究网络协议的数据包级仿真的成本太高。一种替代方法是使用成本较低的流级模型来实现网络仿真。已经提出了几种流量级模型,并在网格/云模拟器中实现了这些模型。出乎意料的是,这些模型的已发布验证(如果有的话)仅包含对几个简单案例的验证。因此,即使使用它们来获得已发布的结果,这些模拟器产生具有科学意义的结果的能力也令人怀疑。这项工作通过与数据包级仿真进行比较,评估了这些TCP通信的最新流级网络模型。虽然可以很容易地显示出先前提出的模型可以产生良好结果的案例,但是我们遵循的是关键方法,该方法将模型反驳置于科学活动的中心,并且我们系统地寻找导致无效结果的案例。对这些案例的仔细分析揭示了根本缺陷,并提出了改进建议。这项工作的一个贡献是,这些改进导致了一个新模型,该模型远非完美,但在网格或云的仿真环境中对所有先前提出的模型进行了改进。也许更重要的贡献是这项工作中遇到的陷阱和意外行为,从而带来了许多启发性的教训。特别是,这项工作表明,仅通过展示(可能有很多)“良好案例”就无法实现模型验证。只有通过尝试证明模型错误的失效方法,才能增强对模型质量的信心。

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