Hadi, MS orcid.org/0000-0003-1422-5254, Lawey, AQ, El-Gorashi, TEH et al. (1 more author) (2018) Big Data Analytics for Wireless and Wired Network Design: A Survey. Computer Networks, 132. pp. 180-199. ISSN 1389-1286
Abstract
Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018, The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | Big Data Analytics; Network design; Self-optimization; Self-configuration; Self-healing network. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/K503836/1 EPSRC EP/H040536/1 EPSRC EP/K016873/1 EPSRC EP/R511717/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Jan 2018 15:30 |
Last Modified: | 20 Aug 2020 01:16 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.comnet.2018.01.016 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126363 |