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SDN helps Big Data to become fault tolerant
Department of Computer Science and Engineering, New Mexico Tech, Socorro, TX, USA .
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-9866-8209
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
Number of Authors: 62018 (English)In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: The Institution of Engineering and Technology , 2018, 1, p. 319-336Chapter in book (Refereed)
Abstract [en]

SDN networks would have many advantages to be used as fault-tolerant Big Data infrastructures such as programmability and global network view which help monitor and control the network behavior adaptively and efficiently. This chapter studied a number of requirements to provide fault tolerance in networks that Big Data applications perform upon. First, we studied the key requirements to be fault tolerant. The network topology design is crucial to provide resiliency against node or link failure. Second, we mentioned the principle concepts of fault tolerance and elaborated on reactive and proactive methods as two common approaches to deal with the failures in networks. Third, the fault-tolerant mechanisms in SDN architecture and their advantages were elucidated. Consequently, we investigated a number of studies that leverage SDN to provide fault tolerance. Finally, this chapter was concluded by introducing open issues and challenges in SDN architecture to provide a perfect fault-tolerant network.

Place, publisher, year, edition, pages
London: The Institution of Engineering and Technology , 2018, 1. p. 319-336
Keywords [en]
telecommunication network topology; Big Data; computer network reliability; software defined networking; failure analysis; software fault tolerance
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-67214DOI: 10.1049/PBPC015E_ch15ISBN: 978-1-78561-304-3 (print)ISBN: 978-1-78561-305-0 (electronic)OAI: oai:DiVA.org:kau-67214DiVA, id: diva2:1202074
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2018-06-27Bibliographically approved

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Publisher's full texthttp://digital-library.theiet.org/content/books/10.1049/pbpc015e_ch15;jsessionid=4af710hogc0kq.x-iet-live-01

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Alizadeh Noghani, KyomaarsTaheri, Javid

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CiteExportLink to record
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Citation style
  • apa
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