Model Based Diagnosis for Network Communication Faults

Leliane Nunes de Barros, Marilza Lemos, Volnys Bernal, and Jacques Wainer

The lack of specialized professionals in network management and the growing complexity of this task has been aiming the need for developing tools to give support to the network administrator task. The construction of such tools requires an intense process of knowledge acquisition from experts in the area as well as the use of Artificial Intelligence (AI) techniques. A number of different approaches have been proposed, evolving from rule-based systems through case-based systems, to more recent modelbased systems [6] [7] [8] [9] [1 i]. A special attention has been given to propose systems to solve two main network management tasks: the fault diagnosis and performance management. The aim of this paper is to specify a Communication Fault Diagnostic System applying the AI Model Based approach.. We claim that this approach provides a foundation for exchanging behavioral, structural and control information between the sub-tasks of such complex systems. We also show what are the main aspects to be considered when constructing such systems: the construction of an automatic network discovery system and a configuration diagnosis system, both to support the construction of the network configuration model, and a network status gathering system to allow the diagnosis system to observe the network.

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