Automated Network Fault Management

Abstract

Future military communication networks will have a mixture of backbone terrestrial, satellite and wireless terrestrial networks. The speeds of these networks vary and they are very heterogeneous. As networks become faster, it is not enough to do reactive fault management. Our approach combines proactive and reactive fault management . Proactive fault management is implemented by dynamic and adaptive routing. Reactive fault management is implemented by a combination of a neural network and an expert system. The system has been developed for the X.25 protocol. Several fault scenarios were modeled and included in the study: reduced switch capacity, increased packet generation rate of a certain application, disabled switch in the X.25 cloud, disabled links. We also modeled occurrence of alarms including severity of the problem, location of the event and a threshold. To detect and identify faults we use both numerical data associated with the performance objects (attributes) in the MIB as well as SNMP traps (alarms). Simulation experiments have been performed in order to understand the convergence of the algorithms, the training of the neural networks involved and the G2/NeurOn-Line software environment and MIB design.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA441009

Entities

People

  • J. S. Baras
  • M. Ball
  • P. Shah
  • P. Viswanathan
  • Salil Gupta

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Communication Networks
  • Computing System Architectures
  • Databases
  • Department Of Defense
  • Digital Communications
  • Electronic Mail
  • Expert Systems
  • Maryland
  • Military Research
  • Network Architecture
  • Network Topology
  • Networks
  • Neural Networks
  • Space Systems
  • Statistics

Fields of Study

  • Computer science

Readers

  • Computer Networking
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  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Fully Networked C3
  • Fully Networked C3 - Command and Control
  • Space