Big data caching for networking: moving from cloud to edge

TLDR

The relentless data tsunami in 5G wireless networks renders traditional spectrum acquisition, base‑station densification, and core‑network scaling ineffective, prompting a shift toward context‑aware edge/cloud computing and big‑data analytics. This work investigates proactive content caching in 5G networks by proposing a big‑data‑enabled architecture. The architecture harnesses large volumes of traffic data to estimate content popularity and strategically caches popular items at base stations, a design validated through a real‑world case study using several hours of Turkish operator traffic and machine‑learning tools. Numerical results demonstrate that, for 16 base stations with 30 % content ratings and 13 GB of storage (78 % of the library), proactive caching achieves 100 % user satisfaction and offloads 98 % of backhaul traffic.

Abstract

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 $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G 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.