Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement
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DOI：
10.1109/infcom.2010.5461930
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Xiaoqiao Meng;V. Pappas;Li Zhang
中科院分区：
文献类型：
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作者：
Xiaoqiao Meng;V. Pappas;Li Zhang
现代数据中心的可扩展性已经成为一个实际问题，近年来引起了人们的极大关注。与需要改变网络架构和路由协议的现有解决方案相比，本文提出使用流量感知虚拟机（VM）放置来提高网络可扩展性。通过优化VM在主机机器上的放置，VM之间的流量模式可以更好地与它们之间的通信距离对齐，例如，具有大的相互带宽使用的VM被分配给非常接近的主机机器。我们制定的VM放置作为一个优化问题，并证明其硬度。我们设计了一个两层的近似算法，有效地解决了非常大的问题大小的VM放置问题。鉴于当前数据中心中的流量模式存在显着差异，以及最近提出的数据中心架构的结构差异，我们进一步对流量模式和网络架构对流量感知VM放置的潜在性能增益的影响进行了比较分析。我们使用从生产数据中心收集的流量跟踪来评估我们提出的VM放置算法，与现有的不利用流量模式和数据中心网络特性的一般方法相比，我们显示出显着的性能改善。
The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.