计算机科学
隐藏物
强化学习
马尔可夫决策过程
GSM演进的增强数据速率
节点(物理)
边缘计算
虚假分享
以内容为中心的网络
缓存算法
边缘设备
智能缓存
方案(数学)
人工智能
分布式计算
计算机网络
云计算
CPU缓存
马尔可夫过程
操作系统
数学分析
统计
数学
结构工程
工程类
作者
Ting Wang,Yuxiang Deng,Jiawei Mao,Mingsong Chen,Gang Liu,Jieming Di,Keqin Li
标识
DOI:10.1109/tmc.2024.3361083
摘要
The tremendous expansion of edge data traffic poses great challenges to network bandwidth and service responsiveness for mobile computing. Edge caching has emerged as a promising method to alleviate these issues by storing a portion of data at the network edge. However, existing caching approaches suffer from either poor caching efficiency with low content-hit ratio or unintelligence of caching policies lacking self-adjustability. In this paper, we propose ICE, a novel Intelligent Edge Caching scheme using a deep reinforcement learning (DRL) method to capture specific valuable information from the requested data. With the benefit of our proposed popularity model based on Newton's law of cooling, ICE fully takes into account the popularity of the contents to be cached and leverages the formulated Markov decision model to decide whether or not the contents should be cached. Moreover, to further improve the caching efficiency, we propose a novel distributed multi-node caching framework, named DCCC, assisted by a multi-tiered caching hierarchy. Comprehensive experiments show that the single-node ICE scheme greatly improves the cache hit rate and contents exchanging time in comparison with both DRL-based and legacy approaches, and our distributed multi-node caching scheme DCCC further significantly improves the overall utilization of caching space.
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