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PREDICTING APPLICATION QUALITY OF EXPERIENCE METRICS USING ADAPTIVE MACHINE LEARNED PROBES

机译:使用自适应机器学习的问题预测经验指标的应用质量

摘要

In general, the disclosure describes techniques for evaluating application quality of experience metrics over a software-defined wide area network. For instance, a network device may receive an application data packet of a data flow. In response to receiving the application data packet, the network device determines whether a packet size of the application data packet is represented in a reference data store. In response to determining that the packet size is not represented in the reference data store, the network device predicts, based on the reference data store, flow metrics for the packet size for each of a plurality of Wide Area Network (WAN) links. The network device selects a WAN link on which to send the application data packet based on the predicted flow metrics.
机译:通常,本公开描述了用于评估在软件定义的广域网上的应用体验质量度量的技术。例如,网络设备可以接收数据流的应用数据分组。响应于接收到应用数据分组,网络设备确定在参考数据存储中是否表示了应用数据分组的分组大小。响应于确定在参考数据存储中未表示分组大小,网络设备基于参考数据存储来预测多个广域网(WAN)链路中的每一个的分组大小的流度量。网络设备根据预测的流量指标选择要在其上发送应用程序数据包的WAN链路。

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