Black-Box Problem Diagnosis in Parallel File Systems

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 peercomparison 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 PostMark), in both PVFS and Lustre clusters.

• Type: Conference
• Year: 2010
• Where: FAST
• Authors: Michael P. Kasick, Jiaqi Tan, Rajeev Gandhi, Priya Narasimhan
• Keywords: Black-box Performance Metrics, FAST 2010, Operating System, Parallel File Systems, Peercomparison Diagnosis Approach