Classification and forecasting of real-time server traffic flows employing long short-term memory for hybrid E/O data center networks

Abstract

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.

© 2021 Optical Society of America

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