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SDN helps velocity in Big Data
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0002-9399-8425
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-7311-9334
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0003-4147-9487
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
2018 (English)In: Big Data and Software Defined Networks / [ed] Javid Taheri, London: The Institution of Engineering and Technology , 2018, 1, p. 207-228Chapter in book (Refereed)
Abstract [en]

Currently, improving the performance of Big Data in general and velocity in particular is challenging due to the inefficiency of current network management, and the lack of coordination between the application layer and the network layer to achieve better scheduling decisions, which can improve the Big Data velocity performance. In this chapter, we discuss the role of recently emerged software defined networking (SDN) technology in helping the velocity dimension of Big Data. We start the chapter by providing a brief introduction of Big Data velocity and its characteristics and different modes of Big Data processing, followed by a brief explanation of how SDN can overcome the challenges of Big Data velocity. In the second part of the chapter, we describe in detail some proposed solutions which have applied SDN to improve Big Data performance in term of shortened processing time in different Big Data processing frameworks ranging from batch-oriented, MapReduce-based frameworks to real-time and stream-processing frameworks such as Spark and Storm. Finally, we conclude the chapter with a discussion of some open issues.

Place, publisher, year, edition, pages
London: The Institution of Engineering and Technology , 2018, 1. p. 207-228
Keywords [en]
Big Data; telecommunication scheduling; parallel processing; software defined networking
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-67213DOI: 10.1049/PBPC015E_ch10ISBN: 978-1-78561-304-3 (print)ISBN: 978-1-78561-305-0 (electronic)OAI: oai:DiVA.org:kau-67213DiVA, id: diva2:1202072
Available from: 2018-04-27 Created: 2018-04-27 Last updated: 2018-06-25Bibliographically approved

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Publisher's full texthttp://digital-library.theiet.org/content/books/10.1049/pbpc015e_ch10;jsessionid=4af710hogc0kq.x-iet-live-01

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Nguyen, Van-GiangBrunström, AnnaGrinnemo, Karl-JohanTaheri, Javid

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