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.
Highlights
Networks generate traffic in rapid, large, and diverse ways, which leads to an estimate of 2.5 exabytes created per day [1]
The results of the above method were compared with a drive test results, where coherence between the two results was demonstrated. Another comparison was conducted with the Key Performance Index (KPI)-based approach and the results were in favor of the proposed approach
To discover and predict such attacks, the authors of [104] proposed a system named Big-distributed Intrusion Detection System (B-dIDS) that relies on two components: 1- HAMR: An in-memory MapReduce engine used for big data processing
Summary
Networks generate traffic in rapid, large, and diverse ways, which leads to an estimate of 2.5 exabytes created per day [1]. In light of the above, the term “Big Data” emerged, and it can be defined as high-volume, high-velocity, and high-variety data that provides substantial opportunities for cost-effective decision-making and enhanced insight through advanced processing which extracts information and knowledge from data [11]. Before commencing the analytics process, data sets may comprise certain consistency and redundancy problems affecting their quality These problems arise due to the diverse sources from which the data originated. This paper is organized as follows: Section 2 presents several case studies uses big data analytics in wireless and wired networks. Case studies of the use of big data analytics for wireless and wired networks
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