Learning algorithms for multicast routing

It is shown how learning algorithms are used to grow shared multicast trees, in order to minimise some performance index such as the average received packet delay or path length. In particular, automata are used to select a core to send a join request to in a dynamic membership environment. The motivation is to improve the performance of shared multicast trees while retaining their attractive scaling properties. It is shown that in the single source (single group) case, automata converge to the optimal shortest path tree solution. For multiple sources, automata reach a `good´ compromise solution. However, automata are most useful in heterogeneous scenarios where the resources are unevenly distributed, a situation which could easily arise due to consumption of resources by multiple priority traffic in future integrated-services networks