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9 - A Proactive and Big Data–Enabled Caching Analysis Perspective

from Part II - Proactive Caching

Published online by Cambridge University Press:  19 October 2020

Thang X. Vu
Affiliation:
Université du Luxembourg
Ejder Baştuğ
Affiliation:
Nokia Bell Labs, France
Symeon Chatzinotas
Affiliation:
Université du Luxembourg
Tony Q. S. Quek
Affiliation:
Singapore University of Technology and Design
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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 Caching
Modeling, Analysis, and Optimization
, pp. 173 - 192
Publisher: Cambridge University Press
Print publication year: 2021

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