Big Data Caching for Networking: Moving from Cloud to Edge

TL;DR

This paper proposes a big data-enabled architecture for proactive content caching in 5G networks, utilizing machine learning on real-world data to improve user satisfaction and reduce backhaul load.

Contribution

It introduces a practical architecture leveraging big data analytics for content caching in 5G, validated through real-world case studies and numerical analysis.

Findings

Achieves 100% user satisfaction with 78% content caching.

Offloads 98% of backhaul traffic in case study.

Demonstrates effectiveness of big data analytics in 5G caching.

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.