GMM-model for Dynamic Multicast Groups using a probabilistic BFS Algorithm

Generalized Multiobjective Multitree model (GMMmodel) considering by the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) was proposed. In this paper, we extends the GMM-model to dynamic multicast groups (i.e., in which egress nodes can change during the connection’s lifetime). If a multicast tree is recomputed from scratch, it may consume a considerable amount of CPU time and all communication using the multicast tree will be temporarily interrupted. To alleviate these drawbacks we propose a Dynamic Generalized Multiobjective Multitree model (Dynamic-GMM-model) that in order to add new egress nodes makes use of a multicast tree previously computed with GMM-model. To solve the Dynamic-GMM-model, a D-GMM algorithm is proposed. In this case, several path between every node in the multicast transmission paths given by the GMMmodel and the new egress node is found using a probabilistic Breadth First Search (BFS) algorithm which the computational time is polynomial. Experimental results considering up to 11 different objectives are presented for the well-known NSF network. We compare the GMM-model performance using MOEA with the proposed Dynamic-GMM-model using D-GMM-BFS algorithm. The main contributions of this paper are the optimization model for dynamic multicast routing; and the heuristic algorithm proposed with polynomial complexity.