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Improving Data Center Network Utilization Using Near-Optimal Traffic Engineering

机译:使用近乎最佳的流量工程提高数据中心网络的利用率

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摘要

Equal cost multiple path (ECMP) forwarding is the most prevalent multipath routing used in data center (DC) networks today. However, it fails to exploit increased path diversity that can be provided by traffic engineering techniques through the assignment of nonuniform link weights to optimize network resource usage. To this extent, constructing a routing algorithm that provides path diversity over nonuniform link weights (i.e., unequal cost links), simplicity in path discovery and optimality in minimizing maximum link utilization (MLU) is nontrivial. In this paper, we have implemented and evaluated the Penalizing Exponential Flow-spliTing (PEFT) algorithm in a cloud DC environment based on two dominant topologies, canonical and fat tree. In addition, we have proposed a new cloud DC topology which, with only a marginal modification of the current canonical tree DC architecture, can further reduce MLU and increase overall network capacity utilization through PEFT routing.
机译:等价多路径(ECMP)转发是当今数据中心(DC)网络中使用最普遍的多路径路由。但是,它无法利用流量工程技术通过分配非均匀链路权重来优化网络资源使用的方式来增加路径分集。就此而言,构建在非均匀链路权重(即不等成本的链路)上提供路径多样性,在路径发现方面的简单性以及在最小化最大链路利用率(MLU)方面的最优性的路由算法是不平凡的。在本文中,我们基于规范和胖树这两种主要拓扑,在云DC环境中实施并评估了惩罚指数流拆分(PEFT)算法。此外,我们提出了一种新的云DC拓扑,只需对当前的规范树DC结构进行少量修改,即可通过PEFT路由进一步降低MLU并提高整体网络容量利用率。

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