Unsupervised Anomaly Event Detection for Cloud Monitoring Using Online Arima

Virtualization offers cost efficient usage of digital resources. Thus, dedicated hardware solutions are transferred into virtualized services running in the cloud. Such softwarization of hardware is for example the IP multimedia subsystems, which telecommunication system providers currently move to the cloud. The dedicated hardware solutions provided a reliability of 99.999% in the past, but the virtualized services come with higher complexity due to the fragile computation stack and cannot provide such high requirements. Zero touch administration of such fragile systems can help to detect automatically anomalies, find root causes and execute remediation actions. This work focusses on the detection of degraded state anomalies. We propose an unsupervised detection approach using the Online Arima forecasting algorithm using real-time monitoring data. This approach is evaluated on a testbed running an open source implementation of the IP multimedia subsystem (Clearwater) executed on a replicated Openstack cloud environment. Results show the applicability of the Online Arima based anomaly detection with high detection rates and low number of false alarms.