Smarter log analysis

E. Aharoni, S. Fine, Y. Goldschmidt, O. Lavi, O. Margalit, M. Rosen-Zvi, L. Shpigelman

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Modern computer systems generate an enormous number of logs. IBM Mining Effectively Large Output Data Yield (MELODY) is a unique and innovative solution for handling these logs and filtering out the anomalies and failures. MELODY can detect system errors early on and avoid subsequent crashes by identifying the root causes of such errors. By analyzing the logs leading up to a problem, MELODY can pinpoint when and where things went wrong and visually present them to the user, ensuring that corrections are accurately and effectively done. We present the MELODY solution and describe its architecture, algorithmic components, functions, and benefits. After being trained on a large portion of relevant data, MELODY provides alerts of abnormalities in newly arriving log files or in streams of logs. The solution is being used by IBM services groups that support IBM xSeries® servers on a regular basis. MELODY was recently tested with ten large IBM customers who use zSeries machines and was found to be extremely useful for the information technology experts in those companies. They found that the solution's ability to reduce extensively large log data to manageable sets of highlighted messages saved them time and helped them make better use of the data.

Original languageEnglish
Article number6032778
JournalIBM Journal of Research and Development
Volume55
Issue number5
DOIs
StatePublished - 1 Sep 2011
Externally publishedYes

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