Data preprocessing for machine-learning-based adaptive data center transmission

Abstract

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.