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Online Detection of Multi-Component Interactions in Production Systems
 

Summary: Online Detection of Multi-Component Interactions
in Production Systems
Adam J. Oliner and Alex Aiken
Department of Computer Science
Stanford University
{oliner, aiken}@cs.stanford.edu
Abstract--We present an online, scalable method for inferring
the interactions among the components of large production sys-
tems. We validate our approach on more than 1.3 billion lines of
log files from eight unmodified production systems, showing that
our approach efficiently identifies important relationships among
components, handles very large systems with many simultaneous
signals in real time, and produces information that is useful to
system administrators.
Keywords-System management, statistical correlation, model-
ing, anomalies, signal compression
I. INTRODUCTION
We are interested in automatic support for understanding
large production systems such as supercomputers, data center
clusters, and complex control systems. Fundamentally, admin-

  

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

 

Collections: Computer Technologies and Information Sciences