论文标题
边缘网络中数据缓存的深度学习调查
A Survey of Deep Learning for Data Caching in Edge Network
论文作者
论文摘要
在新兴5G和移动网络之外的边缘缓存配置的概念是一种有前途的方法，可以处理核心网络中的流量拥塞问题，并减少访问流行内容的延迟。在这方面，最终用户对流行内容的需求可以通过在网络边缘（即与用户近距离接近）的网络边缘进行缓存来满足。除了基于模型的缓存方案外，基于学习的基于学习的边缘缓存优化最近引起了极大的关注，此后的目的是捕获这些基于模型的基于模型和数据驱动技术的最新进展。本文总结了边缘网络中深度学习的利用。我们首先概述了内容缓存的典型研究主题，并根据网络层次结构制定了分类学。然后，提出了许多重要类型的深度学习算法，从监督学习到无监督的学习以及强化学习。此外，从缓存主题和深度学习方法的方面提供了最先进文献的比较。最后，我们讨论了将深度学习用于缓存的挑战和未来的方向
The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching
