Evolutionary Approaches To Minimizing Network Coding Resources
M. Kim, Muriel Médard, Varun Aggarwal, Una-May O'Reilly, Won Bae Kim, Chang Wook Ahn, Michelle Effros · 2007
Abstract — We consider the problem of minimizing the resources used for network coding while achieving the desired throughput in a multicast scenario. Since this problem is NPhard, we seek a method for quickly finding sufficiently good solutions. To this end, we take evolutionary approaches based on a Genetic Algorithm. In this paper, we extend the evolutionary algorithm that we previously proposed in three perspectives. First, whereas the previous algorithm can be applied to only acyclic networks, we devise a modified evaluation method that works also with networks with cycles. Second, we introduce a new set of GA components that in our experiments outperforms the one used in the previous algorithm. Third, we present a new framework of the evolutionary approach, where fitness evaluation and population management are done in a decentralized manner with a limited amount of coordination. The new framework enables a network coding protocol where the resources used for coding are optimized in the setup phase as the proposed evolutionary algorithm being loaded and run at each node of the network. We demonstrate the effectiveness of our algorithms by carrying out simulations on a number of different sets of network topologies. I.