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An Approach of Collecting Performance Anomaly Dataset for NFV Infrastructure

  • Qingfeng Du
  • Yu He
  • Tiandi Xie
  • Kanglin Yin
  • Juan Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11336)

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.

Keywords

Anomaly database NFV Kubernetes IaaS Clearwater Performance monitoring Fault injection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qingfeng Du
    • 1
    • 2
  • Yu He
    • 1
    • 2
  • Tiandi Xie
    • 1
    • 2
  • Kanglin Yin
    • 1
    • 2
  • Juan Qiu
    • 1
    • 2
  1. 1.School of Software EngineeringTongji UniversityShanghaiChina
  2. 2.Software Engineering R&D CentreTongji UniversityShanghaiChina

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