Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

Machine Learning for Network Slicing Resource Management: A Comprehensive Survey

HAN Bin1 and Hans D. Schotten1,2

(1. University of Kaiserslautern, 67663 Kaiserslautern, Germany;
2. German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany)

Abstract: 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.
Keywords: 5G; machine learning; multi-tenancy; network slicing; resource management