Data preprocessing for machine-learning-based adaptive data center transmission
Journal article, 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

Author

Kamran Keykhosravi

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Ahad Hamednia

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Houman Rastegarfar

MathWorks Inc

Erik Agrell

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

ICT Express

24059595 (eISSN)

Vol. 8 1 37-43

Unlocking the Full-dimensional Fiber Capacity

Knut and Alice Wallenberg Foundation (KAW 2018.0090), 2019-07-01 -- 2024-06-30.

Subject Categories

Computer Engineering

Telecommunications

Communication Systems

DOI

10.1016/j.icte.2022.02.002

More information

Latest update

4/5/2022 5