Online Density Grid Pattern Analysis to Classify Anomalies in Cloud and NFV Systems

Technologies like machine-to-machine communication, autonomous driving or virtual reality applications form an increasingly diverse service landscape. This entails individual and dynamic requirements regarding scalability, availability, latency or throughput from the underlying IT infrastructure. To meet those, telecommunication and network providers started a transformation process towards virtualized technologies like network function virtualization (NFV). However, this drastically increases the infrastructure complexity to a point where more autonomous management is required. In order to meet the reliability of dedicated hardware, virtualized solutions are in demand of autonomous recovery and remediation systems. For critical network systems, actions must be selected very cautiously to not disrupt the operational process. To enable a precise handling, anomaly situations need to be accurately identified based on monitoring data streams. Therefore, we present a supervised machine learning method for an online classification of anomaly states based on similarities between anomaly type-specific density grid patterns. For evaluation, we created an extensive NFV testbed running a virtual implementation of the IP multimedia subsystem. Applying our method to classify various synthetically injected anomaly situations, the results reveal an average overall accuracy of 0.94. Further results also show that the classification model is applicable for identifying previously unknown anomaly situations. Thus, our approach provides a valuable step towards autonomous maintenance of virtualized IT infrastructures.