Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

TL;DR

This survey reviews how machine learning techniques are applied to optimize caching strategies in edge networks, focusing on content popularity prediction, user clustering, and cache management in next-generation 5G networks.

Contribution

It provides a comprehensive taxonomy of machine learning methods for edge caching, analyzing recent literature and discussing future research challenges and directions.

Findings

Machine learning improves content popularity estimation.

Clustering users enhances cache efficiency.

Optimized cache placement reduces latency and backhaul traffic.

Abstract

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.