NDLI: BLR-D:applying bilinear logistic regression to factored diagnosis problems

Content Provider	ACM Digital Library
Author	Dunagan, John Duh, Kevin Muniswamy-Reddy, Kiran-Kumar Basu, Sumit
Abstract	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.
Starting Page	31
Ending Page	38
Page Count	8
File Format	PDF
ISSN	01635980
DOI	10.1145/2094091.2094100
Journal	ACM SIGOPS Operating Systems Review (OPSR)
Volume Number	45
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1975-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Logistic regression False discovery rate analysis Factored diagnosis Diagnosis problems Bilinear logistic regression Non-parametric statistics Bilinear models
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Information Systems

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