sisyphus toolkit
Welcome to the sisyphus toolkit!
Version 0.9beta (Nov 5, 2004)
This is a snapshot of some tools created by a project with the
following charter:
With the specific goal of increasing supercomputer RAS (reliability,
availability, and serviceability), we intend to produce a
machine-learning analysis system which enables content-novice
analysts to efficiently understand evolving trends, identify
anomalies, and investigate cause-effect hypotheses in large
multiple-source event log sets.
Currently it provides two independant tools (teirify and slctify)
which address the first two items above by automatically generating
regular expressions of messages in your logfiles, categorized by
increasing anomaly: common, deviant, and anomalous. Common are those
types which occur at least k times (k is an input argument), deviant
are messages which appear fewer than k times but are similar in
content to common messages, and anomalous are messages which are
completely anomalous in content and occurence. A simple GUI is
included for efficient review of results. This provides an efficient
means to define "normal", and thus provides a basis to detect
"abnormal". See pdfs in doc/ieee_cluster04 for more details.
Posted to the log analysis mailing list by Jon Stearley.
http://www.cs.sandia.gov/sisyphus/