Learning algorithms for minimum cost, delay bounded multicast routing in dynamic environments

摘要

Distributed stochastic learning automata (SLA) are used to 'grow' minimum cost delay bounded multicast trees in a dynamic membership environment. It is found that learning automata, which use minimal state information and require only local connectivity knowledge, provide reduced costs over shortest path approaches and comparable static costs to alternative algorithms, by learning to minimise the number of hops taken to join the tree, thereby minimising its resource consumption.