Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
What Supercomputers Say: A Study of Five System Logs
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Using Hidden Semi-Markov Models for Effective Online Failure Prediction
SRDS '07 Proceedings of the 26th IEEE International Symposium on Reliable Distributed Systems
Analyzing system logs: a new view of what's important
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
ACM Computing Surveys (CSUR)
A survey of online failure prediction methods
ACM Computing Surveys (CSUR)
Prediction by categorical features: generalization properties and application to feature ranking
COLT'07 Proceedings of the 20th annual conference on Learning theory
Error log processing for accurate failure prediction
WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
Self-star Properties in Complex Information Systems
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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.