Big data meets telcos: A proactive caching perspective

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.

Big data analytics for wireless and wired network design: A survey

“…[68] Suggested monitoring and analyzing social media and popular sites, to predict and cache certain contents, according to age category, at the predicted locations where these contents are highly demanded. [34] Proposed the use of big data analytics and machine learning techniques to proactively cache popular content in 5G networks. Network optimization [73] Presented three case studies in which a proposed network optimization framework is efficiently utilized.…”

“…An approach, proposed by the authors in [34] , used big data analytics and machine learning to develop a proactive caching mechanism by predicting the popularity distribution of the content in 5G cellular networks. They demonstrated that this approach can achieve efficient utilization of network resources (backhaul offloading) and an enhanced user experience.…”

Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

“…As a matter of fact, the popularity of requested contents follows the Zipf's distribution [23][24][25], which can be expressed as:…”

“…Like the distribution of contents in the web proxies and the traffic dynamics of cellular devices, this kind of power law is used to characterize many real world phenomena [23]. The higher α value corresponds to a steeper distribution, and indicates that a fraction of the content is more popular than the rest of the catalog (i.e., users have very similar interests).…”

Feedforward Neural Networks for Caching

“…The first paper to propose the idea to use machine learning to learn caching rules from available rich data was probably [2]. Nevertheless, the only practical example considered there was collaborative filtering to estimate content popularities at some locations from measurements at other locations.…”