Evolutionary Minimization of Network Coding Resources

Abstract

A method to identify feasible minimal network coding configurations between a source and a set of receivers without altering or modifying the established network infrastructure is proposed. The approach minimizes the resources used for multicast coding while achieving the desired throughput in the multicast scenario. Because the problem of identifying minimal configurations of a graph is known to be NP-hard, our method first identifies candidate minimal configurations and then searches for the optimal ones using a genetic algorithm (GA). Because the optimization process considers the number of coding nodes, the mean number of coding node input links and the sharing of resources by sinks, the problem is thus a multiobjective problem. Two multiobjective algorithms, MOGA and VEGA, are chosen to solve the problem because they are simple enough not to place heavy demands on source nodes when the minimal configuration is sought. The optimization process is investigated by the simulation of a range of randomly generated networks of varying sizes. Performance differences between the multiple-objective GAs are observed, which seem to arise from the difference in their methods of searching. Nevertheless, both methods perform well in terms of identifying feasible minimal configurations with optimized coding resources. The performance is assessed by comparing the optimized solutions with randomly chosen starting configurations. There are always reductions in the number of coding nodes used, typically 50%, and resource sharing is multiplied by several times. Typical mean in-link savings are 10% but may range from zero to close to 30%. We thus show that relatively simple multiple-objective GAs can deliver optimized minimal coding configurations for the network coding multicast problem. Moreover, the approach here offers an improvement over solutions in the literature because our method remains feasible for relatively large networks and its implementation at the source simplifies the functions that must be employed at intermediate nodes.