Finding associations in Grid monitoring data

Error handling is a crucial task in infrastructures as complex as grids. Today, there are several monitoring tools which can be used to report failing grid jobs including corresponding error codes. However, the error codes do not always indicate the actual fault which originally caused the job failure. Human time and expertise is required to manually trace errors back to the real fault underlying an error. We perform Association Rule Mining on grid job monitoring data to automatically retrieve knowledge about the grid components´ behaviour by taking dependencies between grid job characteristics into account. Therewith, problematic grid components are located automatically and this information - expressed by association rules - is visualised in a web interface. This work achieves a decrease in time for fault recovery and consequently yields an improvement of a grid´s reliability.