Evolutionary Approaches to Minimizing Network Coding Resources

TL;DR

This paper introduces evolutionary algorithms to optimize network coding resources efficiently, handling cyclic networks and enabling distributed implementation, significantly improving upon prior sub-optimal solutions in multicast scenarios.

Contribution

The authors develop a new evolutionary approach that extends previous methods to cyclic networks, enriches genetic components, and introduces a distributed framework for resource optimization.

Findings

Significant resource reduction in network coding achieved.

Method outperforms prior algorithms in various topologies.

Distributed implementation enables scalable network coding optimization.

Abstract

We wish to minimize the resources used for network coding while achieving the desired throughput in a multicast scenario. We employ evolutionary approaches, based on a genetic algorithm, that avoid the computational complexity that makes the problem NP-hard. Our experiments show great improvements over the sub-optimal solutions of prior methods. Our new algorithms improve over our previously proposed algorithm in three ways. First, whereas the previous algorithm can be applied only to acyclic networks, our new method works also with networks with cycles. Second, we enrich the set of components used in the genetic algorithm, which improves the performance. Third, we develop a novel distributed framework. Combining distributed random network coding with our distributed optimization yields a network coding protocol where the resources used for coding are optimized in the setup phase by running our evolutionary algorithm at each node of the network. We demonstrate the effectiveness of our approach by carrying out simulations on a number of different sets of network topologies.