Helen J. Wang, John C. Platt, Yu Chen, Ruyun Zhang, and Yi-Min Wang
Technical support contributes 17% of the total cost of ownership of today’s desktop PCs. 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. 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.
|Publisher||Association for Computing Machinery, Inc.|
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