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System- and application-level failures could be characterized by analyzing relevant log files. The resulting data might then be used in numerous studies on and future developments for the mission-critical and large scale computational architecture, including fields such as failure prediction, reliability modeling, performance modeling and power awareness. In this paper, system logs covering a six month period of the Blue Gene/L supercomputer were obtained and subsequently analyzed. Temporal filtering was applied to remove duplicated log messages. Optimistic and pessimistic perspectives were exerted on filtered log information to observe failure behavior within the system. Further, various time to repair factors were applied to obtain application time to interrupt, which will be exploited in further resilience modeling research.
Date of Conference: 16-19 March 2009