Machine Learning for Network Slicing Resource Management: A Comprehensive Survey
This is a comprehensive survey for researchers and practitioners in telecommunications, focusing on incremental applications of machine learning to network slicing.
The paper surveys existing machine learning approaches for managing network slicing resource allocation in 5G cellular networks, highlighting how these methods address challenges in service flexibility and resource efficiency.
The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services, and therewith increases the service flexibility and enhances the network resource efficiency. Meanwhile, it raises new challenges of network resource management. A number of various methods have been proposed over the recent past years, in which machine learning and artificial intelligence techniques are widely deployed. In this article, we provide a survey to existing approaches of network slicing resource management, with a highlight on the roles played by machine learning in them.