State-dependent connection admission control and routing and spectrum assignment in multirate flex-grid optical networks

In this paper we propose an online method for routing and spectrum assignment (RSA) in flex-grid optical networks, which aims at maximizing of a definable reward objective (e.g. carried traffic, economic revenue). The method is based on the theory of Markov Decision Processes (MDP). Connections are admitted and established depending on the traffic load (network state) and on the expected reward increase they bring to the network. In particular, for any incoming request, the routing component of the method recommends the lightpath that yields the highest reward gain in the network. The admission control function accepts the connection if the lightpath gain is positive. A policy iteration algorithm is executed to determine the optimal path and optical channel for each incoming connection request. The method is evaluated for different network scenarios. Furthermore, it is shown how it can provide differentiated grade of service (GoS) for individual connection classes.