可扩展的多目标最优化多播路由(英文)

Multi-Objective Optimization Multicast Routing for Forwarding State Scalability

提出了一种提高多播可扩展性的新思路——将多播可扩展性作为一个最优化目标引入到多播路由算法的设计中,采用多目标最优化路由算法,提高现有多播可扩展性方法的效率.采用多目标最优化路由设计方法对AM(aggregated multicasc)和DTM(dynamic tunnel multicast)两种方法进行改进,给出了相应的最优化目标、启发式多目标最优化多播路由算法和多目标最优化多播路由遗传算法.对于AM方法,使用该算法可以有效地减少汇聚多播树的数量;对于DTM方法,使用该算法可以有效地增加非分枝节点的数量,减少多播状态.

The approach is to include forwarding state scalability as one of the optimal objective when constructing new multicast trees. This multi-objective optimization approach can be applied to many existing multicast state reduction methods. In this paper, the approach is illustrated by applying it to aggregated multicast （AM） and dynamic tunnel multicast （DTM）. Both AM and DTM routing problems are formulated as multiobjective optimization problems, and both heuristic and genetic algorithms are proposed for solving them. Based on the experimental results, the approach can further improve the forwarding state scalability of both approaches by reducing the number of aggregated trees required by the AM method, and by increasing the number of non-branching nodes for the DTM method.