Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey
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Junaid Shuja, Kashif Bilal, Eisa Alanazi, Waleed Alasmary, Abdulaziz Alashaikh, Albert Y. Zomaya
2020
Abstract
Edge networks provide access to a group of proximate users who may have
similar content interests. Caching popular content at the edge networks leads
to lower latencies while reducing the load on backhaul and core networks with
the emergence of high-speed 5G networks. User mobility, preferences, and
content popularity are the dominant dynamic features of the edge networks.
Temporal and social features of content, such as the number of views and likes
are applied to estimate the popularity of content from a global perspective.
However, such estimates may not be mapped to an edge network with particular
social and geographic characteristics. In edge networks, machine learning
techniques can be applied to predict content popularity based on user
preferences, user mobility based on user location history, cluster users based
on similar content interests, and optimize cache placement 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 to lower latencies and increase cache hits. This article surveys the
application of machine learning techniques for caching content in edge
networks. We survey recent state-of-the-art literature and formulate a
comprehensive taxonomy based on (a) machine learning technique, (b) caching
strategy, and edge network. We further survey supporting concepts for optimal
edge caching decisions that require the application of machine learning. These
supporting concepts are social-awareness, popularity prediction, and community
detection in edge networks. 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 towards
caching in edge networks.
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