Guest Editorial: Machine Learning-Enabled Zero Touch Networks

Abstract

With the continued growth of IoT devices and their deployment, manually managing and connecting them is impractical and presents multiple challenges. To that end, Zero Touch Networks (ZTNs) that rely on software-based modules instead of dedicated propriety hardware become a viable potential solution. The overall aim of zero-touch networks is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them. Thus, Zero Touch Networks are able to monitor networks and services and act on faults with minimal (if any) human intervention, including the early detection of emerging problems, autonomous learning, autonomous scaling, remediation, decision making, and support of various optimization objectives. As a result, Zero Touch Networks are able to offer self-serving, self-fulfilling, and self-assuring operations.