Long short-term memory neural networks demonstrate a classification accuracy larger than 99% for highly variable and bursty, real-time server traffic flows. Their performance in terms of forecasting precision displays promising results, both for one-step as well as multi-step predictions. These capabilities make the a priori detection of heavy data streams possible, thus enabling the employment of optical circuit switching.

中文翻译：

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混合E / O数据中心网络采用长短期内存的实时服务器流量的分类和预测

长时短时记忆神经网络对高度可变和突发的实时服务器流量显示出超过99％的分类精度。无论是一步预测还是多步预测，它们在预测精度方面的表现都显示出令人鼓舞的结果。这些功能使对重数据流的事前检测成为可能，从而可以使用光电路交换。