Machine learning based adaptive flow classification for optically interconnected data centers

Nicolaas Viljoen, Houman Rastegarfar, Mingwei Yang, John W Wissinger, Madeleine Glick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We optimize flow placement for a hybrid network implementing an adaptive neural network classifier. We predict elephant flows with high accuracy on anonymized university network traffic. We also demonstrate the capability to perform highly complex actions at 40 Gbps using less than 5% of co-processor capacity. This shows that it is possible to implement intelligent actions such as a neural network in a data center using fully programmable NICs without handicapping the server CPU.

Original languageEnglish (US)
Title of host publication2016 18th International Conference on Transparent Optical Networks, ICTON 2016
PublisherIEEE Computer Society
Volume2016-August
ISBN (Electronic)9781509014675
DOIs
StatePublished - Aug 23 2016
Event18th International Conference on Transparent Optical Networks, ICTON 2016 - Trento, Italy
Duration: Jul 10 2016Jul 14 2016

Other

Other18th International Conference on Transparent Optical Networks, ICTON 2016
CountryItaly
CityTrento
Period7/10/167/14/16

Keywords

  • circuit-switched
  • networks
  • optical interconnects

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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  • Cite this

    Viljoen, N., Rastegarfar, H., Yang, M., Wissinger, J. W., & Glick, M. (2016). Machine learning based adaptive flow classification for optically interconnected data centers. In 2016 18th International Conference on Transparent Optical Networks, ICTON 2016 (Vol. 2016-August). [7550294] IEEE Computer Society. https://doi.org/10.1109/ICTON.2016.7550294