Self-adaptive Lagrange relaxation algorithm for aggregated multicast
解决聚合组播的自适应拉格朗日松弛算法

Multicast has great advantages in data forwarding. But the number of forwarding states becomes huge in routers when there are a large number of multicast groups in the network, which may cause explosions of state information and control information. Aggregated multicast is a new approach to reduce the number of multicast state. It enables multicast groups to share a single distribution tree so that the tree management overhead at core routers can be reduced. Aggregated Multicast can actually be attributed to minimal set cover problem, which is an NP-complete problem. A self-adaptive Lagrange Relaxation Algorithm that can achieve global optimal solution was used to solve it. Simulation results show that this algorithm is better than the conventional greedy algorithm in that it improves aggregation degree and reduces multicast state number.