计算机科学
隐藏物
GSM演进的增强数据速率
能源消耗
体验质量
边缘设备
马尔可夫决策过程
人气
强化学习
带宽(计算)
计算机网络
分布式计算
数据传输
云计算
马尔可夫过程
人工智能
服务质量
数学
生物
操作系统
心理学
生态学
社会心理学
统计
作者
Ting Wang,Jiawei Mao,Mingsong Chen,Gang Liu,Jieming Di,Shui Yu
标识
DOI:10.1109/globecom46510.2021.9685196
摘要
The unprecedented growth of mobile data traffic brings unique challenges for network bandwidth and server resources to meet the diverse QoE (Quality of Experience). Caching becomes a promising way to alleviate these issues by storing a subset of data at the network edge, for which caching policy becomes critical. To this end, various caching schemes have been put forward, however, these schemes are either not intelligent lacking the ability of self-learning and self-decision-making, or inefficient with low data hit rate. Based on these observations, in this paper, we propose a novel Intelligent Caching framework at the Edge, named ICE, via deep reinforcement learning to capture certain valued information of the requested data. Notably, in our approach, the popularity of the data to be cached will be explored and considered. A Markov decision model is further developed to determine whether the data should be cached. The evaluation shows that ICE greatly improves the hit rate in comparison with the state-of-the-art approaches, and reduces the energy consumption for data transmission. Furthermore, based on ICE, the users' QoE is greatly improved. In conclusion, both theoretical analysis and experimental results prove the effectiveness and high performance of ICE compared with conventional strategies.
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