In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains to mobile operators. In this article, proactive content caching in 5G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30% of content ratings and 13 Gbyte of storage size (78% of total library size), proactive caching yields 100% of users' satisfaction and offloads 98% of the backhaul.
Engin Zeydan et al REF made use of the application of Big Data Analytics in mobile cellular networks by the introduction of a proactive caching architecture for 5G wireless networks to process huge amount of available data on a big data platform and leveraging machine learning tools for content popularity prediction to achieve backhaul offloading.
Big Data Caching for Networking: Moving from Cloud to Edge
IEEE Communications Magazine
IEEE Communications Magazine
Computer Science