Evaluation of Network Topology Inference in Opaque Compute Clouds through End-to-End Measurements
Evaluation of Network Topology Inference in Opaque Compute Clouds through End-to-End Measurements
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DOI：
10.1109/cloud.2011.30
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发表时间：
2011-07
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影响因子：
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通讯作者：
Dominic Battré;Natalia Frejnik;Siddhant Goel;O. Kao;Daniel Warneke
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作者：
Dominic Battré;Natalia Frejnik;Siddhant Goel;O. Kao;Daniel Warneke
现代云结构即服务（IaaS）云通过使用硬件虚拟化在资源调配方面提供了前所未有的灵活性和弹性。然而，对于云计算客户来说，这种虚拟化也引入了不透明性，这对数据密集型分布式应用程序造成了严重的障碍。特别地，缺乏网络拓扑信息，即关于租用的虚拟机如何物理互连的信息，可以容易地导致网络瓶颈，因为不能应用利用数据局部性的常用技术。在本文中，我们研究在多大程度上可以推断出基于端到端测量的IaaS云内部的虚拟机的底层网络拓扑。因此，我们实验评估的影响，硬件虚拟化的可测量的链路特性的数据包丢失和延迟使用流行的开源hypervisors KVM和XEN。之后，我们比较了不同的拓扑推理方法的准确性，并提出了一个扩展，以提高推理的准确性，为典型的网络结构在网络中心。我们发现，端到端测量的常见假设在虚拟化的情况下不成立，并且在帕拉虚拟化环境中基于RTT的测量会导致最准确的推断结果。
Modern Infrastructure-as-a-Service (IaaS) clouds offer an unprecedented flexibility and elasticity in terms of resource provisioning through the use of hardware virtualization. However, for the cloud customer, this virtualization also introduces an opaqueness which imposes serious obstacles for data-intensive distributed applications. In particular, the lack of network topology information, i.e. information on how the rented virtual machines are physically interconnected, can easily cause network bottlenecks as common techniques to exploit data locality cannot be applied. In this paper we study to what extent the underlying network topology of virtual machines inside an IaaS cloud can be inferred based on end-to-end measurements. Therefore, we experimentally evaluate the impact of hardware virtualization on the measurable link characteristics packet loss and delay using the popular open source hyper visors KVM and XEN. Afterwards, we compare the accuracy of different topology inference approaches and propose an extension to improve the inference accuracy for typical network structures in datacenters. We found that common assumptions for end-to-end measurements do not hold in presence of virtualization and that RTT-based measurements in Para virtualized environments lead to the most accurate inference results.