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Big Data helps SDN to optimize its controllers
School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0002-2599-8044
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-9194-010X
School of Information Technologies, University of Sydney, Sydney, NSW, Australia .
2018 (English)In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: The Institution of Engineering and Technology , 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.

Place, publisher, year, edition, pages
London: The Institution of Engineering and Technology , 2018, 1. p. 389-408
Keywords [en]
telecommunication traffic; computer network reliability; Big Data; fault tolerance; data analysis; software defined networking
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-67215DOI: 10.1049/PBPC015E_ch19ISBN: 978-1-78561-304-3 (print)ISBN: 978-1-78561-305-0 (electronic)OAI: oai:DiVA.org:kau-67215DiVA, id: diva2:1202077
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
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  • apa
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