Machine Learning - Special issue on inductive transfer
Pinpoint: Problem Determination in Large, Dynamic Internet Services
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
RacerX: effective, static detection of race conditions and deadlocks
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Flashback: a lightweight extension for rollback and deterministic replay for software debugging
ATEC '04 Proceedings of the annual conference on USENIX Annual Technical Conference
Bilinear Discriminant Component Analysis
The Journal of Machine Learning Research
Automatic misconfiguration troubleshooting with peerpressure
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Flight data recorder: monitoring persistent-state interactions to improve systems management
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
From uncertainty to belief: inferring the specification within
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
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Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
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In this paper, we address a pattern of diagnosis problems in which each of J entities produces the same K features, yet we are only informed of overall faults from the ensemble. Furthermore, we suspect that only certain entities and certain features are leading to the problem. The task, then, is to reliably identify which entities and which features are at fault. Such problems are particularly prevalent in the world of computer systems, in which a datacenter with hundreds of machines, each with the same performance counters, occasionally produces overall faults. In this paper, we present a means of using a constrained form of bilinear logistic regression for diagnosis in such problems. The bilinear treatment allows us to represent the scenarios with J+K instead of JK parameters, resulting in more easily interpretable results and far fewer false positives compared to treating the parameters independently. We develop statistical tests to determine which features and entities, if any, may be responsible for the labeled faults, and use false discovery rate (FDR) analysis to ensure that our values are meaningful. We show results in comparison to ordinary logistic regression (with L1 regularization) on two scenarios: a synthetic dataset based on a model of faults in a datacenter, and a real problem of finding problematic processes/features based on user-reported hangs.