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- W3013670178 abstract "Analyzing failures is important for the reliability of HPC systems and failure diagnosis based only on system logs is incomplete. Resource use data - made available recently - is another potential source of data for failure analysis. Recent work that combines analysis of system logs with resource use data show promising results. In this paper, we describe a new workflow for combining system resource usage and failure logs for diagnosis. The workflow - called EXERMEST - identifies significant system counters and events then correlates them to failures and recovery. We apply EXERMEST on the Ranger HPC system cluster log-data and show that it improves diagnosis over previous research. EXERMEST: (i) show that more system counters and errors can be identified only by applying more feature extractors, (ii) identify CPU I/O bottlenecks and Lustre client eviction, (iii) identify network packet drops and Lustre I/O errors, (iv) identify virtual memory and harddisk I/O errors, (v) show that time-bins of different granularities are required for identifying the errors. EXERMEST is available on the public domain for supporting system administrators in failure diagnosis." @default.
- W3013670178 created "2020-04-03" @default.
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- W3013670178 date "2019-12-01" @default.
- W3013670178 modified "2023-09-26" @default.
- W3013670178 title "Using Resource Use Data and System Logs for HPC System Error Propagation and Recovery Diagnosis" @default.
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- W3013670178 doi "https://doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00072" @default.
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