Online anomaly symptom detection and process's resource usage control

In this paper we propose an online lightweight anomaly symptom detection and process's resource usage control mechanism. Our system collects fine-grain resource information that can reflect the subtle changes of the application's behavior. Then it creates models with a learning-based algorithm without manual configurations. If an anomaly symptom is detected, the automatic procedure will start. The system will control the suspected application's resource use by limiting the upper bound resource of the process. The method will make the application yield its CPU to the administrative inspection. In this paper, we described whole architecture of the system and evaluate it with the non deterministic and deterministic failure. Our experimental results indicate that our prototype system is able to detect non deterministic failure with high precision in anomaly training and control it's resource use with an overhead of about 1%.