计算机科学
服务器
云计算
边缘计算
分布式计算
GSM演进的增强数据速率
马尔可夫决策过程
移动边缘计算
强化学习
边缘设备
计算机网络
基站
推荐系统
马尔可夫过程
人工智能
机器学习
操作系统
统计
数学
作者
Chuan Sun,Xiuhua Li,Junhao Wen,Xiaofei Wang,Zhu Han,Victor C. M. Leung
标识
DOI:10.1109/jsac.2023.3235443
摘要
To support rapidly increasing services and applications from users, multi-tier computing is emerged as a promising system-level computing architecture by distributing computing/caching/communication/networking capabilities between cloud servers to users, especially deploying edge servers at network edges (e.g., base stations). However, due to heterogeneous content requests of users and a high-cost hit manner with direct hits, edge caching is still a most serious issue to be addressed. In this paper, we investigate the issue of recommendation-enabled edge caching in mobile two-tier (edge-cloud) computing networks. Particularly, we integrate recommender systems and edge caching to support both direct hits and soft hits and thus improve the resource utilization of edge servers. We model the factors affecting the user quality of experience as a comprehensive system cost and further formulate the problem as a multi-agent Markov decision process with the goal of minimizing the long-term average system cost. To address the formulated problem, we propose a decentralized recommendation-enabled edge caching framework that leverages a discrete multi-agent variant of soft actor-critic and federated learning. The proposed framework enables each edge server to learn its best policy locally and generate judicious decisions independently. Finally, trace-driven simulation results demonstrate that the proposed framework converges to a better caching policy and outperforms several existing algorithms on average system cost reduction.
科研通智能强力驱动
Strongly Powered by AbleSci AI