Abstract
In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation parallel genetic simulated annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.
Highlights
Optical networks [1] have emerged as a promising candidate for next-generation networks providing high channel bandwidth and low communication latency
It is possible that there are no available wavelengths for the multicast tree or the wavelength assignment result leads to poor Quality of Service (QoS) performance
Due to the problem complexity and network dynamics, the network state information cannot be accurate inherently. It is more practical for the user to propose the QoS requirements in a flexible way, e.g., by the delay interval
Summary
Optical networks [1] have emerged as a promising candidate for next-generation networks providing high channel bandwidth and low communication latency. We need to address the issue of QoS multicast in optical networks It means to develop efficient multicast routing algorithms, which can find the cost suboptimal multicast tree and assign wavelengths to it. Every subpopulation evolves for a few generations independently (just like the single population genetic algorithm), and one or more chromosomes are exchanged between these subpopulations. The proposed algorithm is based on the migration model. Multipopulation parallel genetic algorithm and simulated annealing algorithm [7] are two standard techniques for hard combinatorial optimization problems. Our proposed algorithm generates the cost suboptimal multicast tree based on MPGSAA, and assigns wavelengths to the tree.
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