Communication networks scope is growing at a faster rate as new services and new users are being added. Management of such networks becomes important to provide the assured quality of service. Finding the skilled managers is often a problem. To alleviate that and also to provide assistance to the network managers, network management task has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academics and industry. In this paper, a critical review of the network management and artificial intelligence approaches to network management are presented.

In this paper, a method is proposed for network restoration using a centralized, static restoration after failure, where the restoration initiated at the local node or at the source using a hybrid strategy. The traditional approaches to network (re)routing are based on the heuristics that are prone to enter local minima. A new rerouting algorithm is proposed that attempts to identify the (near-)optimal route based on a recurrent neural network that uses simulated annealing. The proposed method for restoration is compared with the ones based on source, local and local destination based rerouting models.

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.