Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests

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 cont...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.59906-59921
Main Authors: Xu, Xiaogeng, Feng, Chunyan, Shan, Siyang, Zhang, Tiankui, Loo, Jonathan
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Caching
Content-centric networks
Delays
dynamic content requests
Edge computing
Heuristic algorithms
Machine learning
Network latency
Nodes
Optimization
Performance gain
proactive caching
Q-learning
Redundancy
Retrieval
Simulation
Traffic delay
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2983068