PeerPressure: A Statistical Method for Automatic Misconfiguration Troubleshooting

Technical support contributes 17% of the total cost of ownership of today’s desktop PCs [20]. An important element of technical support is troubleshooting misconfigured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. In this paper, we present a novel troubleshooting algorithm, PeerPressure , which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [24]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. In PeerPressure , we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation . We have developed a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of them. For the remaining ones, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.