Alarm correlation and network fault resolution using the Kohonen self-organising map

Author

Gardner, Robert D. ; Harle, David A.

Author_Institution

Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow, UK

Volume

3

fYear

1997

fDate

3-8 Nov 1997

Firstpage

1398

Abstract

A vital role for a large telecommunications operator is the ability to effectively manage its network. With increasing system size, complexity and bandwidth threatening to overload current management systems, it is no surprise that network management is a high priority for leading telecommunications providers. In this paper, a novel approach to fault management is investigated in which the advantages of artificial neural network technologies are used to complement and extend the capability of traditional systems. In particular, it is shown how a system based on Kohonen (1990) self-organising map theory can be successfully implemented as a means of identifying fault scenarios arising in an SDH-based environment

Keywords

alarm systems; correlation methods; learning (artificial intelligence); self-organising feature maps; telecommunication computing; telecommunication network management; telecommunication network reliability; Kohonen self-organising map; SDH-based environment; alarm correlation; artificial neural network; bandwidth; fault management; network fault resolution; network management systems; telecommunications operator; training; Artificial neural networks; Bandwidth; Educational institutions; Fault detection; Fault diagnosis; Neural networks; Quality of service; Resource management; Technology management; Telecommunication network management;

fLanguage

English

Publisher

ieee

Conference_Titel

Global Telecommunications Conference, 1997. GLOBECOM '97., IEEE

Conference_Location

Phoenix, AZ

Print_ISBN

0-7803-4198-8

Type

conf

DOI

10.1109/GLOCOM.1997.644365

Filename

644365