By Topic

Identifying symptoms of recurrent faults in log files of distributed information systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Thomas Reidemeister ; Shoshin Distributed Systems Group, E&CE Department, University of Waterloo, Ontario, Canada ; Mohammad A. Munawar ; Paul A. S. Ward

The manual process to identifying causes of failure in distributed information systems is difficult and time-consuming. The underlying reason is the large size and complexity of these systems, and the vast amount of monitoring data they generate. Despite its high cost, this manual process is necessary in order to avoid the detrimental consequences of system downtime. Several studies and operator practice suggest that a large fraction of the failures in these systems are caused by recurrent faults. Therefore, significant efficiency gains can be achieved by automating the identification of these faults. In this work we present methods, which draw from the areas of information retrieval as well as machine learning, to automate the task of infering symptoms pertinent to failures caused by specific faults. In particular, we present a method to infer message types from plain-text log messages, and we leverage these types to train classifiers and extract rules to identify symptoms of recurrent faults automatically.

Published in:

2010 IEEE Network Operations and Management Symposium - NOMS 2010

Date of Conference:

19-23 April 2010