# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Optimization of Next, Generation Cellular Networks
#### 3.2.2 Big Data Aware Next Generation Networks  
As discussed earlier, the extracted insights of big data will be used in improving network service qualities as well as generate new mobile applications. Figure 8 represents big data enabled network architecture consisting of three major stages. The first stage is sources of data. These include user devices, core network, and base system stations. The data evolved from these engines are in raw form. In intelligent big data enabled architectures as in [57], there are storage and computing units at this stage, responsible for edge computing/caching for the provision of optimization solutions. At the second stage, core big data platform exists. This platform collects all the data from different sources. Data processing, storage, filtering, and management are performed at this stage. Lastly, the results generated by big data platform are sent to optimization unit for optimization purpose such as for resources optimization, RAN optimization, etc. Such type of big data-driven architecture is key to implementation of next-generation networks. [PERSON] et al. [58] proposed a big data enabled framework for next-generation network. This architecture is promising for improvement of the performance of the next generation network as it carries significance for signaling procedure, simplified design and standardization procedures. Another framework, showing the viability of big data-driven architecture for mobile communication, is presented in [51]. The authors in [51] described that the maximum utilization of spectrum and improvement in capacity gain is made possible by accurately predicting the user demands, and the accurate prediction is provided by big data analytics. We discuss some major advantages of big data aware next generation networks below.  
Figure 7: Self aware networks layout.  
Cache Management: For an ultra-dense and hyper fast network, cache contents are needed to be organized efficiently. Cache is classified in terms of popularity of the contents. For example, contents such as music or video, which are the most popular, are stored in the cache. Similarly, the contents which are not popular anymore should be removed from the cache. In this way, bandwidth will be efficiently utilized and cache size will be preserved [59].  
Resource Optimization: Mobile subscribers accessing similar contents will share the resources. Meanwhile, if connection is not available with BS, the subscriber or device could ask for assistance from their neighbouring devices or subscribers. Similar idea is presented in [60], where a crowd-enabled data transmission technique is proposed.  
Cloud Processing: Cloud processing is an emerging paradigm for big analysis in big data aware networks. For cloud processing in big data aware networks, distributed framework such as Hadoop is used. Based on mobile traffic pattern, channel resources are reserved in advanced and contents are fed to the BSs. Such a smart behavior mobile traffic pattern could be used to allocate the resources (bandwidth and cache) to a particular location for some on-going event.  
Software Defined Networking: In Software Defined Networking (SDN), a network administrator or engineer can shape network traffic from centralized control plane without touching individual switches/routers and can deliver services wherever needed in the network. Hence, it decouples data plane and control plane. Such decoupling makes network traffic management flexible, where packets are now sent on the basis of new consideration such payload length, application type and quality of services. These are summarized in Table 3.