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This project has been terminated and archived (voted by TSC on  )

Project: Prediction

A failure prediction system could be deployed to help the NFV system avoid the unexpected failure in advance. The whole failure prediction system is made up of a data collector, a failure predictor and a failure management module.

This project aims to development a predictor framework, a data collector model and a dashboard. User could define which failure should be detected and which data should be used for prediction via dashboard.   

The predictor framework is based on machine learning library, e.g. Weka, Spark ML lib. User can define what failure should be detected and which data is used for prediction. The predictor will collect data automatically and perform corresponding prediction. 

The data collector consists of Ceilometer and Monasca which can be extended to plugin some other open source data collectors, e.g. Zabbix, Nagios, Cacti. Based on real-time analytics techniques and machine learning techniques, the failure predictor analyses the data gathered by the data collector to automatically determine whether a failure will happen. If a failure is judged, then the failure predictor sends failure notifications to the failure management module, which could handle these notifications.

In Brahmaputra release, we have defined requirement documents. In release Colorado, we will deliver code, including dashboard, data collector and predictor framework.    

  • Deliverable:
    • Brahmaputra
      • Use cases and gap analysis PDF
  • Workspace

Prediction's data collection model can collect data through Ceilometer, Zabbix and Monasca. User could select metrics in scope of Ceilometer, Zabbix and Monasca for prediction. All metrics are defined below.

Quick links: 

Key Project Facts

Project Name: Prediction
Repo name: prediction
Category: Requirement
Lifecycle State: Proposal approved
Primary Contact:
Project Lead:
Jira Project Name: Prediction
Jira Project Prefix: PREDICTION
Mailing list tag: [PREDICTION]

Link to TSC approval of the project:
Link(s) to approval of additional submitters:

Committers and Contributors


  • Hai Liu,
  • Yijun Yu,
  • Jun Li,
  • Yifei Xue,
  • Linghui Zeng,
  • Lanchao Zheng,
  • Qiao Fu,


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