Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey

作者：Junaid Shuja, Kashif Bilal, Waleed S. Alasmary, H. Sinky, Eisa A. Alanazi · 发表于：Journal of Network and Computer Applications · 年份：2021 · DOI：10.1016/j.jnca.2021.103005 · 被引用次数：127 · 研究领域：Computer Science

Abstract Edge networking is a complex and dynamic computing paradigm that aims to push cloud re-sources closer to the end user improving responsiveness and reducing backhaul traffic. User mobility, preferences, and content popularity are the dominant dynamic features of edge networks. Temporal and social features of content, such as the number of views and likes are leveraged to estimate the popularity of content from a global perspective. However, such estimates should not be mapped to an edge network with particular social and geographic characteristics. In next generation edge networks, i.e., 5G and beyond 5G, machine learning techniques can be applied to predict content popularity based on user preferences, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network. This article investigates the application of machine learning techniques for in-network caching in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (method, objective, and features), (b) caching strategy (policy, location, and replacement), and (c) edge network (type and delivery strategy). A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in th...