An Approach to Failure Prediction in Cluster by Self-updating Cause-and-Effect Graph

摘要

Cluster systems have been widely used in cloud computing, high-performance computing, and other fields, and the usage and scale of cluster systems have shown a sharp upward trend. Unfortunately, the larger cluster systems are more prone to failures, and the difficulty and cost of repairing failures are unusually huge. Therefore, the importance and necessity of failure prediction in cluster systems are obvious. In order to solve this severe challenge, we propose an approach to failure prediction in cluster systems by Self-Updating Cause-and-Effect Graph. Different from the previous approaches, the most novel point of our approach is that it can automatically mine the causality among log events from cluster systems, and set up and update Cause-and-Effect Graph for failure prediction throughout their life cycle. In addition, we use the real logs from Blue Gene/L system to verify the effectiveness of our approach and compare our approach to other approaches using the same logs. The result shows that our approach outperforms other approaches with the best precision and recall rate reaching 89% and 85%, respectively.