.png){kind=icon}
返回
Intelligent edge content caching: A deep recurrent reinforcement learning method
.png){kind=icon}
PRE
AI
DOI：10.1007/s12083-022-01369-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rise of 5G network and the rapid growth of user equipment, there exists a gap between the stringent requirements of emerging applications and the actual functionality of the Internet. In particular, transmitting data over long network links imposes high costs, which can be addressed by the edge caching (EC) method. EC caches the content at the edge server to avoid the extraordinary cost of backhaul link communication. However, in existing EC efforts, it is common to assume either known content popularity or a two-phase caching that is predicted content popularity prior to the caching action, the former being less feasible and the latter increasing the cost of deployment to the real world. A caching strategy is proposed in this paper to cope with this problem that can be feasible end-to-end deployed and has a lower caching cost. Specifically, we first investigate the system cost, including network communication cost, cache over storage cost, and cache replacement cost. And we model the EC problem as a Markov Decision Process (MDP). Then, the Double Deep Recurrent Q Network (DDRQN) algorithm is studied to solve the EC-based MDP problem. Finally, compared with other intelligent caching strategies, the proposed caching strategy can improve the system reward by up to 24% and the cache hit rate by up to 22% under certain conditions.
Keyword:
Edge computing
Edge caching
Edge intelligence
Deep recurrent reinforcement learning
期刊
IF:
2.6
论文数:
2.2K
被引数:
2.9K
机构
引用论文
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing边缘智能: 用边缘计算铺平人工智能的最后一英里PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated LearningIn-Edge AI: 通过联合学习实现移动边缘计算、缓存和通信的智能化IEEE NETWORKOAAI
IEEE NETWORKIF6.3
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks
PREAI
分享收藏Intelligent event-based output feedback control with Q-learning for unmanned marine vehicle systems
PREAI
分享收藏
查看更多内容