Machine Learning based SLA-Aware VNF Anomaly Detection for Virtual Network Management

Since the concept of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) has been proposed, telcos and service providers have leveraged these concepts to provide their services more efficiently. However, as the virtual network in the data centers becomes more complex, a variety of new network management problems arise. To deal with these management problems, it is necessary to monitor and analyze resource usage and traffic load of Virtual Network Functions (VNFs) operating on the virtual network. Recently, there have been many attempts to develop technologies that enable network management without human intervention. In this paper, we specify our anomaly detection problem with scenarios involving SLA violations to satisfy the practical needs of network management. Also, we set the real-world NFV environment to generate anomalous data corresponding to each scenario and extend our approach to implementing the system for root-cause localization which identifies the exact VNF instance causing the SLA-related anomalies. We use the datasets collected from the VNFs’ service function chain scenarios implemented on OpenStack environment, and compare the accuracy of the anomaly detection models generated by various machine learning algorithms. Our experimental results show the best model has F1-measure over 95% for anomaly detection and 93% for root-cause localization.