Multitree-multiobjective multicast routing for traffic engineering

This paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real network topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

Label Space Reduction in MPLS Networks: How Much Can A Single Stacked Label Do?

Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying virtual private networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS-allowing the configuration of multipoint-to-point connections (MP2P)-as a means of reducing label space in LSRs. We found two main drawbacks in this label space reduction a)it should be separately applied to a set of LSPs with the same egress LSR-which decreases the options for better reductions, and b)LSRs close to the edge of the network experience a greater label space reduction than those close to the core. The later implies that MP2P connections reduce the number of labels asymmetricall

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. 1

Generalized Multiobjective Multitree model for Dynamic Multicast Groups

studied for 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 already proposed. In this paper, we extend the GMM-model to dynamic multicast groups (i.e., egress nodes can change during the connection’s lifetime), given that, if recomputed from scratch, it may consume a considerable amount of CPU time. To alleviate this drawback 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 the GMM-model. To solve the Dynamic-GMM-model, a new MASPA (multiobjective approximation using shortest path algorithm) heuristic is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network. We compare the performance of the GMM-model using MOEA with the proposed Dynamic-GMM-model using MASPA, showing that reasonable good solutions may be found using fewre resources (as memory and time). The main contributions of this paper are the optimization model for dynamic multicast routing; and the proposed heuristic algorithm. Keywords–Mathematical programming, multiobjective optimization, traffic engineering, load balancing, dynamic multicast groups. I