An approach of collecting performance anomaly dataset for NFV Infrastructure
Qingfeng Du, Yu Ting He, Tiandi Xie · EasyChair preprint · 2018
Today, Network Function Virtualization(NFV) technology is widely used in industry and academia. At the same time, it presents a lot of challenges to reliability, such as anomaly detection, anomaly location, anomaly prediction and so on. All of these studies need a very large number of anomaly data information. This paper designs a method for collecting anomaly data from IaaS, and constructs a anomaly database for NFV applications. Three types of anomaly data sets are created for anomaly study, includes workload with performance data, fault-load with performance data and violation of Service Level Agreement(SLA) with performance. In order to better simulate the abnormality in the production environment, we use Kubernetes to build a distributed environment, and to accelerate the occurrence of abnormality, a fault injection system is utilized. Our aim is to provide more valuable anomaly data for reliability research in the NFV environment.