Online characterization of buggy applications running on the cloud

As Cloud platforms are becoming more popular, efficient resource management in these Cloud platforms helps the Cloud provider to deliver better quality of service to its customers. In this paper, we present an online characterization method that can identify potentially failing jobs in a Cloud platform by analyzing the jobs' resource usage profile as the job runs. We show that, by tracking the online resource consumption, we can develop a model through which we can predict whether or not a job will have an abnormal termination. We further show, using both real world and synthetic data, that our online tool can raise alarms as early as within the first 1/8th of the potentially failing job's lifetime, with a false negative rate as low as 4%. These alarms can become useful in implementing either one of the following resource-conserving Cloud management techniques: alerting clients early, de-prioritizing jobs that are likely to fail or assigning them less performant resources, deploying or up-regulating diagnostic tools for potentially faulty jobs.