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Data preprocessing for machine-learning-based adaptive data center transmission
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DOI：10.1016/j.icte.2022.02.002.png)
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Abstract
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To enable optical interconnect fluidity in next-generation data centers, we propose adaptive transmission based on machine learning in a wavelength-routing network. We consider programmable transmitters that can apply N possible code rates to connections based on predicted bit error rate (BER) values. To classify the BER, we employ a preprocessing algorithm to feed the traffic data to a neural network classifier. We demonstrate the significance of our proposed preprocessing algorithm and the classifier performance for different values of N and switch port count. (C) 2022 Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences.
Keywords:
Data center network
Neural network
Adaptive transmission
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