Exploiting deductive processes for automated network management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multiagent architecture for network management, which exploits the dynamic reasoning capabilities of the situation calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed on the network devices and is collected by a logical entity for network managing where it is merged with general domain knowledge, with a view to identifying the root causes of faults and to decide on reparative actions. The logical inference system has been devised to carry out automated isolation, diagnosis and even repair of network anomalies, thus enhancing the reliability, performance and security of the network. The relevant results inferred by the logical reasoner and the significant events occurred on the network are stored both in a global DB and in local distributed DBs, in order to enable successive analyses of network events. In order to illustrate the advantages and potential benefits deriving from the reasoning capabilities of our management system, we analyze a case study concerning congestion detection and weight assignment optimization in an intra-domain routing environment.