An Online Performance Anomaly Detector in Cluster File Systems

Performance problems, which can stem from different system components, such as network, memory, and storage devices, are difficult to diagnose and isolate in a cluster file system. In this paper, we present an online performance anomaly detector which is able to efficiently detect performance anomaly and accurately identify the faulty sources in a system node of a cluster file system. Our method exploits the stable relationship between workloads and system resource statistics to detect the performance anomaly and identify faulty sources which cause the performance anomaly in the system. Our preliminary experimental results demonstrate the efficiency and accuracy of the proposed performance anomaly detector.