Optimizing Multiples Objectives in Dynamic Multicast Groups Using a Probabilistic BFS Algorithm

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Executive Summary

Generalized Multiobjective Multitree model (GMMmodel) considering 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, the authors extend the GMM-model to dynamic multicast groups. 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 they propose a Dynamic Generalized Multiobjective Multitree model (D-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 Dynamic-GMM algorithm (D-GMM) is proposed.

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