۱- Introduction

۲- Case studies of the use of big data analytics for wireless and wired networks

۳- Role of big data analytics in cellular network design

۴- The role of big data analytics in SDN & intra-data center networks

۵- The role of big data analytics in optical networks

۶- The role of big data analytics in network security

۷- Big data analytics in the industry

۸- Big data analytics-powered design cycle and challenges

۹- Open research directions

۱۰- Conclusions

References

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.

Introduction

Networks generate traffic in rapid, large, and diverse ways, which leads to an estimate of 2.5 exabytes created per day [1]. There are many contributors to the increasing size of the data. For instance, scientific experiments can generate lots of data, such as CERN’s Large Hadron Collider (LHC) that generates over 40 petabyte each year [2]. Social media also has its share, with over 1 billion users, spending an average 2.5 h daily, liking, tweeting, posting, and sharing their interests on Facebook and Twitter [3]. It is without a doubt that using this activity-generated data can affect many aspects, such as intelligence, e-commerce, biomedical, and data communication network design. However, harnessing the powers of this data is not an easy task. To accommodate the data explosion, data centers are being built with massive storage and processing capabilities, an example of which is the National Security Agency (NSA) Utah data centre that can store up to 1 yottabyte of data [4], and with a processing power that exceeds 100 petaflops [5]. Due to the increased needs to scale-up databases to data volumes that exceeded processing and/or storage capabilities, systems that ran on computer clusters started to emerge. Perhaps the first milestone took place in June 1986 when Teradata [6] used the first parallel database system (hardware and software), with one terabyte storage capacity, in Kmart data warehouse to have all their business data saved and available for relational queries and business analysis [7,8]. Other examples include the Gamma system of the University of Wisconsin [9] and the GRACE system of the University of Tokyo [10].