Many researchers rely on simulations to analyze and validate their researched methods on Cloud infrastructures. However, determining relevant simulation parameters and correctly instantiating them to match the real Cloud performance is a difficult and costly operation, as minor configuration changes can easily generate an unreliable inaccurate simulation result. Using legacy values experimentally determined by other researchers can reduce the configuration costs, but is still inaccurate as the underlying public Clouds and the number of active tenants are highly different and dynamic in time. To overcome these deficiencies, we propose a novel model that simulates the dynamic Cloud performance by introducing noise in the computation and communication tasks, determined by a small set of runtime execution data. Although the estimating method is apparently costly, a comprehensive sensitivity analysis shows that the configuration parameters determined for a certain simulation setup can be used for other simulations too, thereby reducing the tuning cost by up to 82.46 percent, while declining the simulation accuracy by only 1.98 percent on average. Extensive evaluation also shows that our novel model outperforms other state-of-the-art dynamic Cloud simulation models, leading up to 22 percent lower makespan inaccuracy.

中文翻译：

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基于计算和通信噪声的云上简化工作流仿真


许多研究人员依靠模拟来分析和验证他们在云基础设施上的研究方法。然而，确定相关的仿真参数并正确实例化它们以匹配真实的云性能是一项困难且成本高昂的操作，因为微小的配置更改很容易产生不可靠的不准确的仿真结果。使用其他研究人员通过实验确定的遗留值可以降低配置成本，但仍然不准确，因为底层公共云和活跃租户的数量差异很大并且随时间变化。为了克服这些缺陷，我们提出了一种新颖的模型，通过在计算和通信任务中引入噪声来模拟动态云性能，这些噪声由一小组运行时执行数据确定。尽管这种估计方法显然成本高昂，但全面的灵敏度分析表明，针对某个仿真设置确定的配置参数也可以用于其他仿真，从而将调整成本降低高达 82.46%，而仿真精度仅下降 1.98%平均百分比。广泛的评估还表明，我们的新颖模型优于其他最先进的动态云仿真模型，使完工时间误差降低了 22%。