SGER: Algorithm Design for Reconfiguration Problem in Optical Networks

University Of Missouri-Kansas City, Columbia MO

Investigators

Abstract

The optical network is a promising high-speed backbone or transportation network that requires an extremely caution in operations. The reconfiguration is one of the indispensable operations if a virtual topology no longer serves a traffic demand which is changed over time. We propose to develop an algorithm and a complete reconfiguration model for wavelengthrouted optical networks which includes the reconfiguration process and the policy. The reconfiguration process provides the choices of the reconfiguration operations (e.g., add, delete or re-route lightpaths) to maintain the high performance of the network in any traffic demand volumes and patterns while minimally disturbs the current virtual topology. The reconfiguration policy defines which choice to be selected that returns the optimal expected outcome based on the cost of operation and the performance reward. We have preliminary experimental results that show that selecting the choice with the highest immediate outcome does not return the optimal expected outcome in the long term. Although the reconfiguration problem is an NP-hard problem and is a trade-off between performance and number of changes in virtual topology, we can still find the solutions using a multiobjective evolutionary algorithm with the concept of Pareto Optimal. The algorithm provides a set of solutions in the Pareto front while the policy picks one of solutions in the Pareto front that gains the optimal expected outcome. The policy depends on the pattern of traffic. If the future pattern of traffic can be predicted or estimated, the Markov Decision Process (MDP) can define the policy. However, the status of network or the MDP's state which effects the Pareto front needs an intensive study. Our goal is to develop the theory and algorithms in accordance with realistic traffic demand and protocol. The model will be the centralized control over the network, which will not be designed only for the specific performance objective or traffic type but be applicable for various objectives and traffic types. The development of practical algorithms for solving these problems represent an important step in allowing reasonable scale implementation of optical networks. Intellectual Merit of the proposed research rests on the integration between the reconfiguration process and the policy based on the Pareto optimal concept and MDP that has not yet happened in the Optical network reconfiguration field. Reconfiguration problems are among the most difficult in the areas of optical networks since such problems encompass the construction of algorithms involving the extremely high-speed communication over an optical fiber. The consequence problem is how or when to perform the reconfiguration process. From the practical application viewpoint, we want to advance our model to the practical optical networks. Broader Impacts of the proposed research is expected to be strong since we are proposing to integrate two disciplines, the evolutionary computing and the stochastic process for the recon- figuration in the optical networks. We expect that the work will have broad impact in industrial practices on network provisioning, protection and restoration areas.