Fault Management Framework and Multi-layer Recovery Methodology for Resilient System
Carlo Vitucci, Daniel Sundmark, Marcus Jägemar, Jakob Danielsson, Alf Larsson, Thomas Nolte · 2022
Fault management is a key function to guarantee the quality of the service. Research has done a lot to improve fault supervision, and investigation is ongoing in fault prediction, thanks to the potentials of artificial intelligence and machine learning. In this study, we propose a fault management framework that puts an emphasis on fault recovery: a framework developed on multi-layer function and a fault recovery methodology distributed over several technological layers. The basic principle of our proposal is that the system’s complexity exposes it to a higher probability of temporary error. Newfound attention to the fault recovery phase is the key to keeping the service’s quality high and saving maintenance costs by decreasing the return rate.