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  • 1.
    Cho, Daewoong
    et al.
    School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
    Bastani, Saeed
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Zomaya, Albert Y
    School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
    Big Data helps SDN to optimize its controllers2018In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: IET Digital Library, 2018, 1, p. 389-408Chapter in book (Refereed)
    Abstract [en]

    In this chapter, we first discuss the basic features and recent issues of the SDN control plane, notably the controller element. Then, we present feasible ideas to address the SDN controller-related problems using Big Data analytics techniques. Accordingly, we propose that Big Data can help various aspects of the SDN controller to address scalability issue and resiliency problem. Furthermore, we proposed six applicable scenarios for optimizing the SDN controller using the Big Data analytics: (i) controller scale-up/out against network traffic concentration, (ii) controller scale-in for reduced energy usage, (iii) backup controller placement for fault tolerance and high availability, (iv) creating backup paths to improve fault tolerance, (v) controller placement for low latency between controllers and switches, and (vi) flow rule aggregation to reduce the SDN controller's traffic. Although real-world practices on optimizing SDN controllers using Big Data are absent in the literature, we expect scenarios we highlighted in this chapter to be highly applicable to optimize the SDN controller in the future.

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