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A Reliability-oriented Faults Taxonomy and a Recovery-oriented Methodological Approach for Systems Resilience


Publication Type:

Conference/Workshop Paper


46th Annual IEEE Computers, Software, and Applications Conference






Fault management is an important function that impacts the design of any digital system, from the simple kiosk in a shop to a complex 6G network. It is common to classify fault conditions into different taxonomies using terms like fault or error. Fault taxonomies are often suitable for managing fault detection, fault reporting, and fault localization but often neglect to support all different functions required by a fault management process. A correctly implemented fault management process must be able to distinguish between defects and faults, decide upon appropriate actions to recover the system to an ideal state, and avoid an error condition. Fault management is a multi-disciplinary process where recovery actions are deployed promptly by combined hardware, firmware, and software orchestration. The importance of fault management processes significantly increases with modern nanometer technologies, which suffer the risk of so-called soft errors, a corruption of a bit cells that can happen due to spurious disturbance, like cosmic radiation. Modern fault management implementations must support recovery actions for soft errors to ensure a steady system. This paper describes an extended fault classification model that emphasizes fault management and recovery actions. We aim to show how the reliability-based fault taxonomy definition is more suitable for the overall fault management process.


author = {Carlo Vitucci and Daniel Sundmark and Marcus J{\"a}gemar and Jakob Danielsson and Alf Larsson and Thomas Nolte},
title = {A Reliability-oriented Faults Taxonomy and a Recovery-oriented Methodological Approach for Systems Resilience},
isbn = {9781665488105},
editor = {IEEE},
month = {June},
year = {2022},
booktitle = {46th Annual IEEE Computers, Software, and Applications Conference},
publisher = {IEEE},
url = {}