An AI-driven Malfunction Detection Concept for NFV Instances in 5G

TL;DR

This paper proposes an AI-based method using semi-supervised learning to detect malfunctions in NFV applications within 5G networks, enabling autonomous responses like roll-backs to maintain network stability.

Contribution

It introduces a novel AI-driven malfunction detection concept for NFV instances in 5G, utilizing application profiling and semi-supervised learning for autonomous fault management.

Findings

Effective detection of abnormal NFV application behavior

Enables autonomous rollback to prevent network outages

Reduces operational costs through self-organizing network management

Abstract

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.