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Machine Learning-Based Traffic Prediction for Optical Switching Resource Allocation in Hybrid Intra-Data Center Networks

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Abstract

This work presents a highly accurate traffic prediction model based on nonlinear autoregressive neural networks for efficient forecasting of heavy traffic streams in intra-data center networks. Its deep-learning version guarantees a prediction error of 10−10.

© 2019 The Author(s)

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