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DETECTING TRANSIENT VS. PERPETUAL NETWORK BEHAVIORAL PATTERNS USING MACHINE LEARNING

机译:检测瞬态VS。使用机器学习的永久网络行为模式

摘要

In one embodiment, a network assurance service that monitors a network detects a pattern of network measurements from the network that are associated with a particular network problem. The network assurance service tracks characteristics of the detected pattern over time. The network assurance service uses the tracked characteristics of the detected pattern over time as input to a machine learning-based pattern analyzer. The pattern analyzer is configured to determine whether the detected pattern is a perpetual or transient pattern in the network, and the pattern analyzer is further configured to detect anomalies in the characteristics of the pattern. The network assurance service initiates a change to the network based on an output of the machine learning-based pattern analyzer.
机译:在一个实施例中,监视网络的网络保证服务从网络检测与特定网络问题相关联的网络测量的模式。网络保证服务随时间跟踪检测到的模式的特征。网络保证服务将随时间推移所检测到的模式的跟踪特征用作基于机器学习的模式分析器的输入。模式分析器被配置为确定检测到的模式是网络中的永久模式还是瞬态模式,并且模式分析器被进一步配置为检测模式特征中的异常。网络保证服务基于基于机器学习的模式分析器的输出来发起对网络的更改。

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