Stochastically Guaranteed Routing for Additive Link Metrics with Unknown Distributions

Network applications that are in need of some level of guarantees from the network to operate, such as multimedia programs, are not well served with the conventional best-effort service of the IP-based networks. The difficulty of finding preferential paths for those applications is compounded by the intrinsic inaccuracies of the network state information maintained by the nodes that have to make such decisions. We use a probabilistic modeling and a framework to select paths for applications that want more cooperation from the network to operate satisfactorily. The links are associated with additive link metrics. We represent the stochasticity of links by means of a new composite metric composed of an interval with a lower and upper bound and an associated probability. The interpretation and relevance of our metric is such that in the next decision time period the expected value of the resource is between the upper and the lower bound with the associated probability. Three simple and straightforward methods of computing our composite metric are presented. An algorithm, called Augmented-Dijkstra Additive Metric (ADAM), with the same complexity as the standard Dijkstra algorithm, provides an effective solution for statistical additive link metric (such as delay) guarantees. Simulation results conducted in ns2 evaluate and confirm the effectiveness of our approach.