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Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks

机译:在CLOS型数据中心网络中自适应拟合网络拓扑到交通局部

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Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's topological architecture, design its addressing and routing schemes, and validate its adaptively fitting capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.
机译:本地化的流量在今天的数据中心无处不在。在这方面,我们建议将底层网络的拓扑结构符合交通局部,以便我们可以提高网络资源利用率的效率。我们使我们的网络基础架构成为多样化的,从某种意义上是它的拓扑可以安装到流量的轮廓中。我们描述了我们的网络拓扑结构,设计了其寻址和路由方案,并验证了其自适应拟合能力。我们还评估其性能并将其与脂肪树进行比较,代表性的拼盘型数据中心网络架构。评估结果表明,我们的网络可以将与脂肪树相同的吞吐量,但使用的网络资源显着降低。

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