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A Framework for Classification and Visualization of Elephant Flows in SDN-Based Networks

机译:基于SDN的网络中大象流的分类和可视化框架

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Long-lived flows termed as elephant flows normally transport large volumes of data in enterprise networks, particularly data center networks. These flows tend to consume a lot of bandwidth and fill up network buffers end-to-end. This causes non-trivial delays for short-lived flows referred to as mice flows which are usually delay-sensitive. Therefore, identifying and handling elephant flows is important for QoS provisioning. In this paper, we present a framework for real-time detection and visualization of elephant flows in SDN-based networks using sFlow. Using our proposed framework, network operators can examine elephant flows through each switch by double-clicking the switch node in the topology visualization UI. Although not in the scope of this paper, but in order to meet traffic engineering requirements, the elephant flows detected and visualized by our proposed framework can be reprioritized, re-scheduled, or routed via dedicated high speed links. We evaluate the proposed framework by using a physical SDN testbed as well as a Mininet-based testbed.
机译:长期流被称为“大象流”,通常在企业网络(尤其是数据中心网络)中传输大量数据。这些流往往会消耗大量带宽,并端到端填满网络缓冲区。这会导致短暂流(称为鼠标流)的非平凡延迟,通常对延迟敏感。因此,识别和处理大象流对于QoS设置很重要。在本文中,我们提出了一个使用sFlow实时检测和可视化基于SDN的网络中的大象流的框架。使用我们提出的框架,网络运营商可以通过双击拓扑可视化UI中的交换机节点来检查通过每个交换机的流量。尽管不在本文讨论范围之内,但是为了满足交通工程的要求,我们提出的框架所检测到的和可视化的大象流可以通过优先的高速链路进行重新排序,重新安排或路由。我们通过使用物理SDN测试平台和基于Mininet的测试平台来评估建议的框架。

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