Time-Varying Network Modeling And Its Optimal Routing Strategy

Since all the existing real world networks are evolving, the study of traffic dynamics is a challenging task. Avoidance of traffic congestion, system utility maximization and enhancement of network capacity are prominent issues. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time-varying data communication networks (TVCN) based on the dynamics of inflowing links. Traffic congestion can be avoided by using a suitable centrality measure, especially betweenness and Eigen vector centralities. If the nodes coming in user’s route are most betweenness central, then that route will be highly congested. Eigen vector centrality is used to find the influence of a node on others. If a node is most influential, then it will be highly congested and considered as least reputed. For that reason, routes are chosen such that the sum of the centralities of the nodes coming in user’s route should be minimum. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user’s path with lowest betweenness centrality and highest reputation will always give maximum rate at the stable point.