Proactive edge caching in content-centric networks with massive dynamic content requests

Xu, X.G., Feng, C.Y., Zhang, T.K. and Loo, Jonathan ORCID: (2020) Proactive edge caching in content-centric networks with massive dynamic content requests. IEEE Access, 8. 59906 -59921.

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Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes.

Item Type: Article
Identifier: 10.1109/ACCESS.2020.2983068
Keywords: Content-centric networks, dynamic content requests, edge computing, proactive caching, Q-learning
Subjects: Computing > Systems > Computer networking
Computing > Systems > Distributed computing
Computing > Intelligent systems
Depositing User: Jonathan Loo
Date Deposited: 14 May 2020 11:07
Last Modified: 06 Feb 2024 16:02


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