A Proactive and Big data-enabled Caching Analysis Perspective

Publication Type:

Book Chapter

Source:

Wireless Edge Caching: Modelling, Analysis, and Optimization, Cambridge University Press (Submitted)

Abstract:

Large-scale data analysis is becoming an important source of information for Mobile Network Operators (MNOs). MNOs can now investigate the feasibility of possible new technological advances such as storage/memory utilization, context-awareness and edge/cloud computing using analytic platforms designed for big data processing. Within this context, studying caching from mobile data traffic analytical perspective can offer rich insights on evaluating the potential benefits and gains of proactive caching at base stations. In this chapter, we study how data collected from MNOs can be leveraged using machine learning tools in order to infer insights into the benefits of caching. Through our practical architecture, vast amount of data can be harnessed for content popularity estimations and placing strategic contents at BSs. Our results demonstrate several gains in terms of both content demand satisfaction and backhaul offloading rates while utilizing real-world datasets collected from a major MNO.