Evaluating machine learning algorithms for anomaly detection in clouds
Anton Gulenko, Marcel Wallschläger, Florian Schmidt, Odej Kao, Feng Liu · 2016
Critical services in the field of Network Function Virtualization require elaborate reliability and high availability mechanisms to meet the high service quality requirements. Traditional monitoring systems detect overload situations and outages in order to automatically scale out services or mask faults. However, faults are often preceded by anomalies and subtle misbehaviors of the services, which are overlooked when detecting only outages. We propose to exploit machine learning techniques to detect abnormal behavior of services and hosts by analysing metrics collected from all layers and components of the cloud infrastructure. Various algorithms are able to compute models of a hosts normal behavior that can be used for anomaly detection at runtime. An offline evaluation of data collected from anomaly injection experiments shows that the models are able to achieve very high precision and recall values.