دانلود رایگان مقاله انگلیسی تحلیل کلان داده برای طراحی شبکه بی سیم و سیمی - الزویر 2018

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.

Conclusions

There are many areas in which big data analytics can be utilized in the network design process. The concept of gathering network data and correlating them with user trends and service requirements can indeed create an adaptive and user-centric network design. Throughout our survey, we noticed a lot of focus on the field of wireless communication networks design using big data. Delving deeper reveals that the field of 5G is getting the majority of the researchers’ attention due to the new opportunities it has to offer. The optical networking, inter-DC and SDN fields, on the other hand, have yet further research challenges to tackle. We also note that the integration of SDN and big data analytics would facilitate the perfection of the design cycle. The field of network security also has its share where big data analytics is utilized to detect security threats. Industrial efforts toward optimizing networks based on big data analytics reflect the increasing trend toward employing AI-like approaches, such as pattern recognition and machine learning for network design. Some of the considered solutions handle big data in a batch manner while others are capable of performing real-time processing. Handling big data in a batch mode can offer more accurate information at the expense of delayed results due to the size of the processed data, while real-time processing offers fast results at the expense of accuracy. Hence, it would be an application-dependent decision whether to choose the former or the latter option. We predict that the field of network design based on big data analytics will continue to flourish in the near future as more data are collected from the networks and processed to extract useful information regarding network behavior. In the far future, or maybe quite soon, as some claim, employing quantum computing for machine learning purposes could help in dethroning Moor’s law and provide more processing space per unit time. This extra space can be harnessed for big data analytics employed in network design.