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Towards H-SDN Traffic Analytic Through Visual Analytics and Machine Learning

  • Tin Tze Chiang
  • Tan Saw ChinEmail author
  • Lee Ching Kwang
  • Y. Zulfadzli
  • K. Rizaludin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11611)

Abstract

With new networking paradigm emerged through Software-Defined Networking (SDN) offering various networking advantages over the traditional paradigm, organizations are attracted to migration their legacy networks to SDN networks. However, it is both challenging and impractical for organizations to migrate from traditional network architecture to full SDN architecture overnight. Therefore, the migration plan is performed in stages, resulting in a new type of network termed hybrid SDN (H-SDN). Effective migration and traffic scheduling in H-SDN environment are the two areas of challenges organizations face. Various solutions have been proposed in the literatures to address these two challenges. Differing from the approaches taken in the literatures, this work utilizes visual analytic and machine learning to address the two challenges. In both full SDN and H-SDN environment, literatures showed that data analytics applications have been successfully developed for various purposes as network security, traffic monitoring and traffic engineering. The success of data analytic applications is highly dependent on prior data analysis from both automated processing and human analysis. However, with the increasing volume of traffic data and the complex networking environment in both SDN and H-SDN networks, the need for both visual analytic and machine learning in inevitable for effective data analysis of network problems. Hence, the objectives of this article are three-folds: Firstly, to identify the limitations of the existing migration plan and traffic scheduling in H-SDN, followed by highlighting the challenges of the existing research works on SDN analytics in various network applications, and lastly, to propose the future research directions of SDN migration and H-SDN traffic scheduling through visual analytics and machine learning. Finally, this article presents the proposed framework termed VA-hSDN, a framework that utilizes visual analytics with machine learning to meet the challenges in SDN migration and traffic scheduling.

Keywords

H-SDN Migration Scheduling Visual analytic 

Notes

Acknowledgments

This research work is fully supported by the research grant of TM R&D and Multimedia University, Cyberjaya, Malaysia. We are very thankful to the team of TM R&D and Multimedia University for providing the support to our research studies.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tin Tze Chiang
    • 1
  • Tan Saw Chin
    • 1
    Email author
  • Lee Ching Kwang
    • 2
  • Y. Zulfadzli
    • 2
  • K. Rizaludin
    • 3
  1. 1.Faculty of Informatics and ComputingMultimedia UniversityCyberjayaMalaysia
  2. 2.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia
  3. 3.Telekom Malaysia R&DJalan Persiaran MulitmediaCyberjayaMalaysia

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