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    Computer Science Department, Politehnica University of Bucharest
    Research Institute for Artificial Intelligence

    Event Log Mining Tool for Large Scale HPC Systems

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    Event log files are the most common source of information for the characterization of events in large scale systems. However the large size of these files makes the task of manual analysing log messages to be difficult and error prone. This is the reason why recent research has been focusing on creating algorithms for automatically analysing these log files. In this paper we present a novel methodology for extracting templates that describe event formats from large datasets presenting an intuitive and user-friendly output to system administrators. Our algorithm is able to keep up with the rapidly changing environments by adapting the clusters to the incoming stream of events. For testing our tool, we have chosen 5 log files that have different formats and that challenge different aspects in the clustering task. The experiments show that our tool outperforms all other algorithms in all tested scenarios achieving an average precision and recall of 0.9, increasing the correct number of groups by a factor of 1.5 and decreasing the number of false positives and negatives by an average factor of 4.

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    Description

    Title : Event Log Mining Tool for Large Scale HPC Systems
    Author(s) : Ana Gainaru, Franck Cappello, Stefan Trausan-Matu, Bill Kramer
    Abstract : Event log files are the most common source of information for the characterization of events in large scale systems. However the large size of these files makes the task of manual analysing log messages to be difficult and error prone. This is the reason why recent research has been focusing on creating algorithms for automatically analysing these log files. In this paper we present a novel methodology for extracting templates that describe event formats from large datasets presenting an intuitive and user-friendly output to system administrators. Our algorithm is able to keep up with the rapidly changing environments by adapting the clusters to the incoming stream of events. For testing our tool, we have chosen 5 log files that have different formats and that challenge different aspects in the clustering task. The experiments show that our tool outperforms all other algorithms in all tested scenarios achieving an average precision and recall of 0.9, increasing the correct number of groups by a factor of 1.5 and decreasing the number of false positives and negatives by an average factor of 4.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2011

    Affiliations Computer Science Department, Politehnica University of Bucharest
    Editors : Emmanuel Jeannot, Raymond Namyst, Jean Roman
    Journal : Lecture Notes in Computer Science
    Volume : 6852
    Publisher : Springer Berlin Heidelberg
    Pages : 52-64
    Url : http://www.springerlink.com/content/3877h3380j712800/
    Doi : 10.1007/978-3-642-23400-2

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    Stefan's Peer Evaluation activity

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