The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

中文翻译：

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NSGA-II 和 NSGA-III 用于云中自动缩放参数扫描实验的比较分析

云计算范式专注于提供可靠且可扩展的虚拟基础架构，以提供执行和存储服务。这种范式特别适合解决资源贪婪的科学计算应用，例如参数扫描实验 (PSE)。通过实施自动缩放器，可以通过在调度应用程序任务时获取或终止虚拟机 (VM) 实例来扩展和缩减虚拟基础架构。在本文中，我们扩展了一项现有研究，该研究以称为多目标进化自动缩放器 (MOEA) 的最先进自动缩放器为中心。MOEA 使用多目标优化算法来确定可能的虚拟基础设施设置集。在这种情况下，MOEA 的性能很大程度上受所使用的底层优化算法及其调整的影响。因此，我们分析了两种著名的多目标进化算法（NSGA-II 和 NSGA-III）以及它们如何影响 MOEA 自动缩放器的性能。三个真实世界 PSE 的模拟实验表明，当使用 NSGA-III 代替 NSGA-II 时，MOEA 得到显着改善，因为前者提供了更好的开发与探索权衡。