Data preprocessing for machine-learning-based adaptive data center transmission
Artikel i vetenskaplig tidskrift, 2022

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.

Data center network

Neural network

Adaptive transmission

Författare

Kamran Keykhosravi

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk, Kommunikationssystem

Ahad Hamednia

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Houman Rastegarfar

MathWorks

Erik Agrell

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk, Kommunikationssystem

ICT Express

24059595 (eISSN)

Vol. 8 1 37-43

Frigöra full fiberoptisk kapacitet

Knut och Alice Wallenbergs Stiftelse (KAW 2018.0090), 2019-07-01 -- 2024-06-30.

Ämneskategorier

Datorteknik

Telekommunikation

Kommunikationssystem

DOI

10.1016/j.icte.2022.02.002

Mer information

Senast uppdaterat

2022-04-05