Automation of 5G Network Slice Control Functions with Machine Learning

Abstract

5G communication networks will be complex due to the emergence of an unprecedented huge number of new types of connected devices and services. Moreover, the on-demand creation of virtual network slices, each suitable for a different application, is posing challenges to the efficient management of network resources, while optimally satisfying the quality of service requirements in time-varying workloads and network conditions. This article, which is tutorial in nature, introduces 5G network slices (from the point of view of the non-wireless part of the network) and elaborates the necessity of automation of network functions related to the design, construction, deployment, operation, control, and management of network slices. It revisits machine learning techniques applicable to the automation of network functions. It then presents a machine-learning-based framework for the operation and control of network slices by continuously monitoring workload, performance, and resource utilization, and dynamically adjusting the resources allocated to network slices. Preliminary results of workload prediction accuracy obtained from the analysis of real-life data collected from a web server are also reported.