Self-adaptive Lagrange Relaxation Algorithm for Aggregated Multicast

Authors:
Hua Wang;Zuquan Ge;Jun Ma
Affiliations:
School of Computer Science and Technology, Shandong University, Jinan, Shandong Province, P.R. China;School of Computer Science and Technology, Shandong University, Jinan, Shandong Province, P.R. China;School of Computer Science and Technology, Shandong University, Jinan, Shandong Province, P.R. China
Venue:
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Year:
2007

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Abstract

Multicast has great advantages in data forwarding. But the number of forwarding states becomes huge in routers when there are large numbers of multicast groups in the network, which may cause explosions of state information and control information. Aggregated multicast is a novel approach to reducing multicast state numbers. 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. To solve it this paper proposes a self-adaptive Lagrange Relaxation Algorithm, which can achieve global optimal solution. Simulation results show that this algorithm is better than the conventional greedy algorithm in that it improves aggregation degree and reduces multicast state number.