Performance degradation in communication networks can be viewed to be caused by a set of faults, called soft failures, owing to which the network resources like bandwidth can not be utilized to the expected level. An automated solution to the performance management problem involves identifying these soft failures and use/suggest suitable remedies to tune the network for better performance. Abductive reasoning model is identified as a suitable candidate for the network performance management problem. An approach to solve this problem using the realistic abductive reasoning model is proposed. The realistic abductive inference mechanism is based on the parsimonious covering theory with some new features added to the general abductive reasoning model. The network performance management knowledge is assumed to be represented in the most general form of causal chaining, namely, hyper-bipartite network. Ethernet performance management is taken up as a case study. The results obtained by the proposed approach demonstrate its effectiveness in solving the network performance management problem.

In this paper, we propose a method to solve the network fault diagnosis problem using the realistic abductive reasoning model. This model uses abductive inference mechanism based on the parsimonious covering theory and adds some new features to the general model of diagnostic problem solving. The network fault diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite graph. A layered graph is constructed from the given hyper-bipartite graph by the addition of few dummy nodes. Then the diagnostic problem is solved starting from the bottom-most layer of the layered graph, as a series of bipartite graphs, until the top-most layer is reached. The inference mechanism using realistic abductive reasoning model to diagnose the faults in communication network is symptom driven, based on some application programs, and hypothesis-test paradigm is used to refine the solution space. We demonstrate the fault diagnostic capability of the proposed inference model by considering one node of a given network, where the management information would be used to diagnose its local problems and connectivity of the node in the network. The results obtained by the proposed model substantiate its effectiveness in solving the network fault diagnostic problems.

In this paper, a method for optimal test sequence generation using Multiple Unique Input/Output Sequences (MUIOS) is presented. The test sequence generation problem using MUIOS is viewed as an asymmetric travelling sales person problem and is solved using simulated annealing methodology. This technique is applied to various test cases and the results are compared with the results obtained by using rural Chinese postman algorithm.