Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introduction
- Part I Optimal Cache Placement and Delivery
- Part II Proactive Caching
- 7 Learning Popularity for Proactive Caching in Cellular Networks
- 8 Wireless Edge Caching for Mobile Social Networks
- 9 A Proactive and Big DataEnabled Caching Analysis Perspective
- 10 Mobility-Aware Caching in Cellular Networks
- Part III Cache-Aided Interference and Physical Layer Management
- Part IV Energy-Efficiency, Security, Economic, and Deployment
- Index
9 - A Proactive and Big DataEnabled Caching Analysis Perspective
from Part II - Proactive Caching
Published online by Cambridge University Press: 19 October 2020
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introduction
- Part I Optimal Cache Placement and Delivery
- Part II Proactive Caching
- 7 Learning Popularity for Proactive Caching in Cellular Networks
- 8 Wireless Edge Caching for Mobile Social Networks
- 9 A Proactive and Big DataEnabled Caching Analysis Perspective
- 10 Mobility-Aware Caching in Cellular Networks
- Part III Cache-Aided Interference and Physical Layer Management
- Part IV Energy-Efficiency, Security, Economic, and Deployment
- Index
Summary
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 a 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 base stations (BSs).Ourresults demonstrate several gains in terms of both content demand satisfaction and backhaul offloading rates while utilizing real-world data sets collected from a major MNO.
- Type
- Chapter
- Information
- Wireless Edge CachingModeling, Analysis, and Optimization, pp. 173 - 192Publisher: Cambridge University PressPrint publication year: 2021
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