论文标题
基于模型和机器学习的基于缓存网络的缓存和路由算法
Model and Machine Learning based Caching and Routing Algorithms for Cache-enabled Networks
论文作者
论文摘要
在不久的将来，网络内缓存可能会成为各种网络系统（例如5G网络，LPWAN和IoT Systems）的组成部分。在本文中，我们比较了基于模型和机器学习方法，用于设计缓存和路由策略以提高缓存网络性能（例如，延迟，命中率）。我们首先概述了设计基于模型的策略的关键原理，并讨论了这些方法获得的分析结果和界限。通过对现实世界轨迹和网络进行实验，我们将内容流行度和请求流相关性之间的相互作用视为影响缓存性能的重要因素。关于路由，我们表明影响性能的主要因素是替代路径路由和内容搜索。然后，我们讨论了多个机器学习模型的适用性，特别是加固学习，深入学习，转移学习和概率图形模型，以缓存和路由问题。
In-network caching is likely to become an integral part of various networked systems (e.g., 5G networks, LPWAN and IoT systems) in the near future. In this paper, we compare and contrast model-based and machine learning approaches for designing caching and routing strategies to improve cache network performance (e.g., delay, hit rate). We first outline the key principles used in the design of model-based strategies and discuss the analytical results and bounds obtained for these approaches. By conducting experiments on real-world traces and networks, we identify the interplay between content popularity skewness and request stream correlation as an important factor affecting cache performance. With respect to routing, we show that the main factors impacting performance are alternate path routing and content search. We then discuss the applicability of multiple machine learning models, specifically reinforcement learning, deep learning, transfer learning and probabilistic graphical models for the caching and routing problem.
