A Tale of Two Topologies: Exploring Convertible Data Center Network Architectures with Flat-tree
A Tale of Two Topologies: Exploring Convertible Data Center Network Architectures with Flat-tree
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DOI：
10.1145/3098822.3098837
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发表时间：
2017-08
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通讯作者：
Yiting Xia;Xiaoye Sun;Simbarashe Dzinamarira;Dingming Wu;Xin Sunny Huang;T. Ng
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作者：
Yiting Xia;Xiaoye Sun;Simbarashe Dzinamarira;Dingming Wu;Xin Sunny Huang;T. Ng
本文提出了可转换的数据中心网络架构，它可以动态改变网络拓扑以结合多种架构的优点。我们提出扁平树原型架构作为实现这一概念的第一步。 Flat-tree 可以实现为 Clos 网络，然后转换为不同大小的近似随机图，从而实现类似 Clos 的实现简单性和类似随机图的传输性能。我们提出了网络架构和控制系统的详细设计。使用真实数据中心流量跟踪的模拟表明，扁平树能够使用不同的拓扑选项来优化各种工作负载。我们在 20 台交换机 24 台服务器测试台上实现了一个示例扁平树网络。拓扑变化后流量在2.5s内达到最大吞吐量，证明了运行时转换拓扑的可行性。仅通过将拓扑从Clos转换为近似随机图，网络核心带宽就增加了27.6%。这种改进可以转化为应用程序的加速，因为我们观察到 Spark 和 Hadoop 作业中的通信时间减少了。
This paper promotes convertible data center network architectures, which can dynamically change the network topology to combine the benefits of multiple architectures. We propose the flat-tree prototype architecture as the first step to realize this concept. Flat-tree can be implemented as a Clos network and later be converted to approximate random graphs of different sizes, thus achieving both Clos-like implementation simplicity and random-graph-like transmission performance. We present the detailed design for the network architecture and the control system. Simulations using real data center traffic traces show that flat-tree is able to optimize various workloads with different topology options. We implement an example flat-tree network on a 20-switch 24-server testbed. The traffic reaches the maximal throughput in 2.5s after a topology change, proving the feasibility of converting topology at run time. The network core bandwidth is increased by 27.6% just by converting the topology from Clos to approximate random graph. This improvement can be translated into acceleration of applications as we observe reduced communication time in Spark and Hadoop jobs.