Black-Box Problem Diagnosis in Parallel File Systems
Proceedings of the 8th USENIX Conference on File and Storage Technologies (FAST '10),
San Jose, CA, February 2010.
Michael P. Kasick, Jiaqi Tan*, Rajeev Gandhi, Priya Narasimhan
Parallel Data Laboratory
School of Computer Science & Electrical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
*DSO National Laboratories Singapore
We focus on automatically diagnosing different performance problems in parallel file systems by identifying, gathering and analyzing OS-level, black-box performance metrics on every node in the cluster. Our peer-comparison diagnosis approach compares the statistical attributes of these metrics across I/O servers, to identify the faulty node. We develop a root-cause analysis procedure that further analyzes the affected metrics to pinpoint the faulty resource (storage or network), and demonstrate that this approach works commonly across stripe-based parallel file systems. We demonstrate our approach for realistic storage and network problems injected into three different file-system benchmarks (dd, IOzone, and Post-Mark), in both PVFS and Lustre clusters.
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