An AI-driven Malfunction Detection Concept for NFV Instances in 5G
This addresses network management challenges for 5G operators by enabling more efficient and cost-effective operations, though it appears incremental as it builds on existing AI and NFV/SDN paradigms.
The paper tackles the problem of detecting malfunctions in Network Function Virtualization (NFV) instances in 5G networks by proposing an AI-driven concept using semi-supervised learning to create profiles for reference and enable autonomous roll-back to prevent outages.
Efficient network management is one of the key challenges of the constantly growing and increasingly complex wide area networks (WAN). The paradigm shift towards virtualized (NFV) and software defined networks (SDN) in the next generation of mobile networks (5G), as well as the latest scientific insights in the field of Artificial Intelligence (AI) enable the transition from manually managed networks nowadays to fully autonomic and dynamic self-organized networks (SON). This helps to meet the KPIs and reduce at the same time operational costs (OPEX). In this paper, an AI driven concept is presented for the malfunction detection in NFV applications with the help of semi-supervised learning. For this purpose, a profile of the application under test is created. This profile then is used as a reference to detect abnormal behaviour. For example, if there is a bug in the updated version of the app, it is now possible to react autonomously and roll-back the NFV app to a previous version in order to avoid network outages.