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A novel network virtualization based on data analytics in connected environment

  • Khac-Hoai Nam Bui
  • Sungrae Cho
  • Jason J. Jung
  • Joongheon Kim
  • O-Joun Lee
  • Woongsoo Na
Original Research

Abstract

Big data analytics is a growing trend for network and service management. Some approaches such as statistical analysis, data mining and machine learning have become promising techniques to improve operations and management of information technology systems and networks. In this paper, we introduce a novel approach for network management in terms of abnormality detection based on data analytics. Particularly, the main research focuses on how the network configuration can be automatically and adaptively decided, given various dynamic contexts (e.g., network interference, heterogeneity and so on). Specifically, we design a context-based data-driven framework for network operation in connected environment which includes three layer architecture: (i) network entity layer; (ii) complex semantic analytics layer and (iii) action provisioning layer. A case study on interference-based abnormal detection for connected vehicle explains more detail about our work.

Keywords

Big data analytics Network virtualization Heterogeneous network Connected environment Network interference Data-driven networking Machine learning techniques 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A41015675).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Khac-Hoai Nam Bui
    • 1
  • Sungrae Cho
    • 1
  • Jason J. Jung
    • 1
  • Joongheon Kim
    • 1
  • O-Joun Lee
    • 1
  • Woongsoo Na
    • 1
  1. 1.Department of Computer EngineeringChung-Ang UniversitySeoulSouth Korea

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