Big Data Meets Telcos: A Proactive Caching Perspective

TL;DR

This paper explores proactive caching in 5G networks using big data analytics to improve content delivery efficiency, demonstrating significant gains in request satisfaction and backhaul offloading based on real user data and simulations.

Contribution

It introduces a big data framework for estimating content popularity and evaluates proactive caching benefits in 5G networks using real traffic data and numerical simulations.

Findings

Proactive caching can achieve 100% request satisfaction with sufficient storage.

Backhaul offloading of 98% is possible with 16 base stations.

Content popularity estimation improves caching efficiency.

Abstract

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.