Automatic Fault Characterization Via Abnormality-Enhanced Classification
Source: Purdue University
Enterprise and high-performance computing systems are growing extremely large and complex, employing many processors and diverse software/hardware stacks. As these machines grow in scale, faults become more frequent and system complexity makes it difficult to detect and diagnose them. The difficulty is particularly large for faults that degrade system performance or cause erratic behavior but do not cause outright crashes. The cost of these errors is high since they significantly reduce system productivity, both initially and by time required to resolve them. Current system management techniques, do not work well since they require manual examination of system behavior and do not identify root causes.