An approach of collecting performance anomaly dataset for NFV infrastructure

Abstract

Network Function Virtualization (NFV) technology is widely used in industry and academia. Meanwhile, it brings a lot of challenges to the NFV applications’ reliability, such as anomaly detection, anomaly location, anomaly prediction and so on. All of these studies need a large number of anomaly data information. This paper designs a method for collecting anomaly data from Infrastructure as a Service (IaaS), and constructs an anomaly database for NFV applications. Three types of anomaly datasets are created for anomaly study, including datasets of workload with performance data, fault-load with performance data and violation of Service Level Agreement (SLA) with performance. In order to simulate an anomaly in a production environment better, we use Kubernetes to build a distributed environment, and to accelerate the occurrence of anomalies, a fault injection system is utilized. Our aim is to provide more valuable anomaly data for reliability research in NFV environments.