Analyzing System Logs: A New View of What’s Important
نویسندگان
چکیده
System logs, such as the Windows Event log or the Linux system log, are an important resource for computer system management. We present a method for ranking system log messages by their estimated value to users, and generating a log view that displays the most important messages. The ranking process uses a dataset of system logs from many computer systems to score messages. For better scoring, unsupervised clustering is used to identify sets of systems that behave similarly. We propose a new feature construction scheme that measures the difference in the ranking of messages by frequency, and show that it leads to better clustering results. The expected distribution of messages in a given system is estimated using the resulting clusters, and log messages are scored using this estimation. We show experimental results from tests on xSeries servers. A tool based on the described methods is being used to aid support personnel in the IBM xSeries support center.
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