Failure Analysis of Jobs in Compute Clouds: A Google Cluster Case Study

In this paper, we analyze a workload trace from the Google cloud cluster and characterize the observed failures. The goal of our work is to improve the understanding of failures in compute clouds. We present the statistical properties of job and task failures, and attempt to correlate them with key scheduling constraints, node operations, and attributes of users in the cloud. We also explore the potential for early failure prediction, and anomaly detection for the jobs. Based on our results, we speculate that there are many opportunities to enhance the reliability of the applications running in the cloud, such as pro-active maintenance of nodes or limiting job resubmissions. We further find that resource usage patterns of the jobs can be leveraged by failure prediction techniques. Finally, we find that the termination statuses of jobs and tasks can be clustered into six dominant categories based on the user profiles.