Multidimensional Analysis of System Logs in Large-scale Cluster Systems
Wei Zhou, Jianfeng Zhan, Dan Meng
TL;DR
This paper introduces a multidimensional graph mining approach for analyzing multi-source system logs to improve failure analysis in large-scale cluster systems, offering more comprehensive and precise failure insights.
Contribution
It presents a novel multidimensional analysis method using graph mining to analyze system logs from multiple sources, enhancing failure detection accuracy.
Findings
More complete failure knowledge obtained
Higher analysis precision achieved
Effective in large-scale cluster systems
Abstract
It is effective to improve the reliability and availability of large-scale cluster systems through the analysis of failures. Existed failure analysis methods understand and analyze failures from one or few dimension. The analysis results are partial and with less precision because of the limitation of data source. This paper presents multidimensional analysis based on graph mining to analyze multi-source system logs, which is a promising failure analysis method to get more complete and precise failure knowledge.
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Reliability and Analysis Research · Service-Oriented Architecture and Web Services