Abstract
Mobile Edge Computing (MEC) is a technology that enables on-demand the provision of computing and storage services as close to the user as possible. In an MEC environment, frequently visited content can be deployed and cached upon edge servers to boost the efficiency of content delivery and thus improving user-perceived experience. However, due to the dynamic nature of MEC, it remains a great challenge how to fully exploit mobility information in yielding high-quality content caching decisions for delay-sensitive real-time mobile applications. To address this challenge, this paper proposes a novel mobility-aware caching method by leveraging a Multi-Agent Deep Reinforcement Learning-Based (MAACC) Approach model. The proposed method synthesizes a content fitness algorithm for estimating the priority of caching content with high user fitness and a collaborative caching strategy built upon a multi-agent deep reinforcement learning model. Empirical results clearly show that MAACC outperforms its peers regarding cache hit rate and transfer delay time.
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Acknowledgment
This article is supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065) and Domestic Visiting Program of Jiangxi Normal University. Additionally, this work is supported in part by Henan Province Science and Technology Projects (232102210024).
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Zhao, H. et al. (2024). A Multi-Agent Deep Reinforcement Learning-Based Approach to Mobility-Aware Caching. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_5
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