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 c ore to send a join request to in a dynamic membership environment. The moti vation is to improve the performance of shared multicast trees while retain ing their attractive scaling properties. It is shown that in the single sou rce (single group) case, automata converge to the optimal shortest path tre e solution. For multiple sources, automata reach a 'good' compromise soluti on. However, automata are most useful in heterogeneous scenarios where the resources are unevenly distributed, a situation which could easily arise du e to consumption of resources by multiple priority traffics in future integ rated-services networks.