INDUCTION AND DEDUCTION FOR AUTONOMOUS NETWORKS

The key issues in network management are the representation and sharin g of management information and the automatic management mechanisms ba sed on the underlying information infrastructure. In this paper, we pr opose a framework, which operates on the standard MIB's and CMIP, for the network management system with learning and inference as its manag ement engines. In addition to the general domain knowledge, patterns r elated to the managed network are learned to enhance the understanding of the network and refine the knowledge base. Facts in object-oriente d databases or queries from management applications trigger the infere nce process on logical rules which are either prespecified knowledge o r learned network patterns. Forward inference drives prediction and co ntrol, while backward inference directs diagnosis and supports view ab straction. A case study on ATM network topolgy tuning is presented.