Abstract
With the explosive growth of smart devices and mobile applications, mobile core networks face the challenge of exponential growth in traffic and computing demand. Edge caching is one of the most promising solutions to the problem. The main purpose of edge caching is to place popular content that users need at the edge of the network, borrow free space to reduce user waiting time, and lighten the network load by reducing the amount of duplicate data. Due to the promising advantages of edge caching, there have been many efforts motivated by this topic. In this paper, we have done an extensive survey on the existing work from our own perspectives. Distinguished from the existing review articles, our work not only investigates the latest articles in this area, but more importantly, covers all the researches of the total process of edge caching from caching placement optimization, policy design, to the content delivery process. In particular, we discuss the benefits of caching placement optimization from the perspective of different stakeholders, detail the delivery process, and conduct in-depth discussions from the five phases, i.e., requested content analysis, user model analysis, content retrieval, delivery, and update. Finally, we put forward several challenges and potential future directions, and hope to bring some ideas for the follow-up researches in this area.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We have investigated lots of related articles published in the past five years, most of which focused on caching placement optimization, policies and delivery processes, and selecting articles with high relevance and significant contributions in this field to investigate the edge caching comprehensively
On the Internet of Vehicles (IoV), Bitaghsir et al [38] proposed a caching placement algorithm based on Multi-Armed Bandit Learning, which selects the content to be cached in the roadside unit (RSU) according to the content popularity, and uses the user social characteristics to select the optimal caching path
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The work of Goian et al [18] focused on the caching technology of popular content, providing a comparison between traditional and popularity-based caching, and proposed that popularity prediction in edge caching is crucial for service providers and users. Yao et al [19] conducted a detailed investigation on mobile edge caching and compared different caching policies according to the impact of different caching locations and different caching performance indicators on caching strategies They did not discuss the impact of users’ social attributes on the Sensors 2021, 21, 5033 edge caching separately, nor did they pay attention to the different stakeholders in the edge caching.
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