Unsupervised Anomaly Event Detection for VNF Service Monitoring Using Multivariate Online Arima

Abstract

Cloud computing provides companies large scale access to virtual resources, offering cost efficient and flexible usage of digital resources at any time. Thus, companies digitalize their dedicated hardware solutions to virtualized services, which can run in a cloud environment. For example, telecommunication providers move their IP multimedia subsystems, which currently run on dedicated hardware, into the cloud. As the dedicated hardware solutions provided a reliability of 99.999% in the past, the same high reliability is demanded for the virtualized services. But these come with higher complexity due to the fragile computation stack and cannot provide such high requirements. Future zero touch administration systems can help to detect automatically anomalies, find root causes and execute automated remediation actions, providing, providing the opportunity to increase the reliability of the overall system. This work focusses on the detection of degraded state anomalies. We propose an unsupervised detection approach using a multivariate version of the Online Arima forecasting algorithm consuming 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. Results show the applicability of the Online Arima detection approach with high detection rates and low number of false alarms.