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

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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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.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Computing, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Tin Tze Chiang & Tan Saw Chin

  2. Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Lee Ching Kwang & Y. Zulfadzli

  3. Telekom Malaysia R&D, Jalan Persiaran Mulitmedia, 63100, Cyberjaya, Selangor, Malaysia

    K. Rizaludin

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  1. Tin Tze Chiang
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  5. K. Rizaludin
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Corresponding author

Correspondence to Tan Saw Chin .

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Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Huazhong University of Science and Technology, Wuhan, China

    Jun Feng

  3. Fordham University, New York City, NY, USA

    Md Zakirul Alam Bhuiyan

  4. University of New Brunswick, Fredericton, NB, Canada

    Rongxing Lu

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Tze Chiang, T., Saw Chin, T., Ching Kwang, L., Zulfadzli, Y., Rizaludin, K. (2019). Towards H-SDN Traffic Analytic Through Visual Analytics and Machine Learning. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11611. Springer, Cham. https://doi.org/10.1007/978-3-030-24907-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-24907-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24906-9

  • Online ISBN: 978-3-030-24907-6

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