Measuring network throughput in the cloud: The case of Amazon EC2
Measuring network throughput in the cloud: The case of Amazon EC2
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DOI：
10.1016/j.comnet.2015.09.037
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
V. Persico;Pietro Marchetta;A. Botta;A. Pescapé
中科院分区：
文献类型：
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作者：
V. Persico;Pietro Marchetta;A. Botta;A. Pescapé
云提供商采用复杂的虚拟化技术和策略，在大量不协调和相互不信任的客户之间共享资源。共享网络环境特别要求在虚拟机之间划分网络资源的机制。同时，部署在这些虚拟机上的应用的性能可能受到底层网络的性能的严重影响，因此受到这种机制的严重影响。然而，由于安全和商业原因，提供商很少提供有关网络组织，性能和监管机制的详细信息。此外，科学文献只提供了云内部网络性能的模糊图像。少数可用的先驱作品稍微集中在这方面，使用不同的方法，在少数有限的情况下操作，或报告相互矛盾的结果。在本文中，我们提出了一个详细的分析亚马逊EC2的内部网络的性能，通过采用一个非cooperativeexperimental评估方法（即不依赖于提供商的支持）。我们的目标是提供一个定量评估的网络性能作为一个功能的几个变量，如地理区域，资源价格或规模。我们提出了一种执行此类分析的详细方法，我们认为这在如此复杂和动态的环境中至关重要。在此分析过程中，我们发现并分析了Amazon在允许的最大吞吐量方面对客户流量的限制。由于我们的工作，可以理解提供商为了管理其基础设施而实施的复杂机制如何影响云客户感知的性能，并可能篡改以前在文献中提出的监控和控制方法。利用我们对带宽限制机制的了解，我们可以清楚地了解Amazon EC2网络中可实现的最大吞吐量，并阐明何时以及如何实现这种最大吞吐量以及成本。
Cloud providers employ sophisticated virtualization techniques and strategies for sharing resources among a large number of largely uncoordinated and mutually untrusted customers. The shared networking environment, in particular, dictates the need for mechanisms to partition network resources among virtual machines. At the same time, the performance of applications deployed over these virtual machines may be heavily impacted by the performance of the underlying network, and therefore by such mechanisms. Nevertheless, due to security and commercial reasons, providers rarely provide detailed information on network organization, performance, and mechanisms employed to regulate it. In addition, the scientific literature only provides a blurred image of the network performance inside the cloud. The few available pioneer works marginally focus on this aspect, use different methodologies, operate in few limited scenarios, or report conflicting results.In this paper, we present a detailed analysis of the performance of the internal network of Amazon EC2, performed by adopting anon-cooperativeexperimental evaluation approach (i.e. not relying on provider support). Our aim is to provide a quantitative assessment of the networking performance as a function of the several variables available, such as geographic region, resource price or size. We propose a detailed methodology to perform this kind of analysis, which we believe is essential in a such complex and dynamic environment. During this analysis we have discovered and analyzed the limitations enforced by Amazon over customer traffic in terms of maximum throughput allowed. Thanks to our work it is possible to understand how the complex mechanisms enforced by the provider in order to manage its infrastructure impact the performance perceived by the cloud customers and potentially tamper with monitoring and controlling approaches previously proposed in literature. Leveraging our knowledge of the bandwidth-limiting mechanisms, we then present a clear picture of the maximum throughput achievable in Amazon EC2 network, shedding light on when and how such maximum throughput can be achieved and at which cost.