Scientific workflow applications on Amazon EC2

G. Juve, E. Deelman, K. Vahi, Gaurang Mehta, G. Berriman, Benjamin P. Berman, P. Maechling
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引用次数: 282

Abstract

The proliferation of commercial cloud computing providers has generated significant interest in the scientific computing community. Much recent research has attempted to determine the benefits and drawbacks of cloud computing for scientific applications. Although clouds have many attractive features, such as virtualization, on-demand provisioning, and “pay as you go” usage-based pricing, it is not clear whether they are able to deliver the performance required for scientific applications at a reasonable price. In this paper we examine the performance and cost of clouds from the perspective of scientific workflow applications. We use three characteristic workflows to compare the performance of a commercial cloud with that of a typical HPC system, and we analyze the various costs associated with running those workflows in the cloud. We find that the performance of clouds is not unreasonable given the hardware resources provided, and that performance comparable to HPC systems can be achieved given similar resources. We also find that the cost of running workflows on a commercial cloud can be reduced by storing data in the cloud rather than transferring it from outside.
Amazon EC2上的科学工作流应用程序
商业云计算提供商的激增引起了科学计算社区的极大兴趣。最近的许多研究都试图确定云计算在科学应用中的利弊。尽管云有许多吸引人的特性,比如虚拟化、按需供应和基于使用的“按需付费”定价,但它们是否能够以合理的价格提供科学应用程序所需的性能还不清楚。本文从科学工作流应用的角度考察了云计算的性能和成本。我们使用三个特征工作流来比较商业云与典型HPC系统的性能,并分析与在云中运行这些工作流相关的各种成本。我们发现,在提供硬件资源的情况下,云的性能并不是不合理的,并且在提供类似资源的情况下,可以实现与HPC系统相当的性能。我们还发现,通过将数据存储在云中而不是从外部传输数据,可以降低在商业云上运行工作流的成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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