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Using Correlated Surprise to Infer Shared Influence Adam J. Oliner, Ashutosh V. Kulkarni, and Alex Aiken
 

Summary: Using Correlated Surprise to Infer Shared Influence
Adam J. Oliner, Ashutosh V. Kulkarni, and Alex Aiken
Stanford University
Department of Computer Science
{oliner, ashutosh.kulkarni, aiken}@cs.stanford.edu
Abstract
We propose a method for identifying the sources of prob-
lems in complex production systems where, due to the pro-
hibitive costs of instrumentation, the data available for
analysis may be noisy or incomplete. In particular, we may
not have complete knowledge of all components and their
interactions. We define influences as a class of component
interactions that includes direct communication and re-
source contention. Our method infers the influences among
components in a system by looking for pairs of components
with time-correlated anomalous behavior. We summarize
the strength and directionality of shared influences using
a Structure-of-Influence Graph (SIG). This paper explains
how to construct a SIG and use it to isolate system mis-
behavior, and presents both simulations and in-depth case

  

Source: Aiken, Alex - Department of Computer Science, Stanford University

 

Collections: Computer Technologies and Information Sciences