Symptom-based Fault Detection in Modern Computer Systems

Abstract

Miniaturization and the increasing number of components, which get steadily more complex, lead to a rising failure rate in modern computer systems. Especially soft hardware errors are a major problem because they are usually temporary and therefore hard to detect. As classical fault-tolerance methods are very costly and reduce system efficiency, light-weight methods are needed to increase system reliability. A method that copes with this requirement is symptom-based fault detection. In this work, we evaluate the ability to detect different faults with symptom-based fault detection by using hardware performance counters. As the knowledge of a fault occurrence is usually not enough, we also evaluate the possibility to make conclusions about which fault occurred. For the evaluation, we used the fault-injection library FINJ and manually manipulated loops. The results show that symptom-based fault detection enables the system to detect faulty application behavior, however fine-grained conclusions about the causing fault are hardly possible.