Big data caching for networking: moving from cloud to edge

Abstract

In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more 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 for 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, a vast amount of data is harnessed for content popularity estimation, and strategic contents are cached at BSs to achieve higher user satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours worth of mobile data traffic is collected from a major telecom operator in Turkey, and 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 in terms of both user satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30 percent of content ratings and 13 GB storage size (78 percent of total library size), proactive caching yields 100 percent user satisfaction and offloads 98 percent of the backhaul.