Deploying MIB data mining for proactive network management

P. G. Kulkarni, S. I. McClean, G. P. Parr, M. M. Black

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of Proactive Network Management that underpins an acceptable Quality of Service. This paper proposes a framework for achieving congestion avoidance through Proactive Network Management using data mining. It examines the inter-relationships between network element Management Information Base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identify network exception conditions.

Original languageEnglish
Title of host publication2006 3rd International IEEE Conference Intelligent Systems, IS'06
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
Event2006 3rd International IEEE Conference Intelligent Systems, IS'06 - London, United Kingdom
Duration: Sept 4 2006Sept 6 2006


Conference2006 3rd International IEEE Conference Intelligent Systems, IS'06
Country/TerritoryUnited Kingdom


  • Classification
  • Congestion management
  • Data mining
  • Feature selection using statistical t-test
  • Proactive network management

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence


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