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An approach of collecting performance anomaly dataset for NFV Infrastructure

EasyChair Preprint no. 494

13 pagesDate: September 8, 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.

Keyphrases: Anomaly database, Clearwater, Fault Injection., IaaS, Kubernetes, NFV, performance monitoring

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Qingfeng Du and Yu He and Tiandi Xie},
  title = {An approach of collecting performance anomaly dataset for NFV Infrastructure},
  howpublished = {EasyChair Preprint no. 494},
  doi = {10.29007/8b62},
  year = {EasyChair, 2018}}
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