  Edge networking is a complex and dynamic computing paradigm that aims to push
cloud resources 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 the taxonomy. Moreover, we debate
research challenges and future directions for optimal caching decisions and the
application of machine learning in edge networks.
