An adaptive strategy for de ection routing in meshes

In this paper we describe a new adaptive routing algorithm for meshed topology de ection networks Our algorithm is based on a local learning method which evolves in order to produce a local spatial representation of the tra c We rst prove that our algorithm is a generalization of the Z routing Secondly we prove that we can set the parameters of the learning algorithm such that our adaptive policy cannot create livelock situation Then we show experimentally the e ciency of our algorithm First we compare the routing policies in a grid network under an uniform load Second we create local congestion in order to show that the adaptive routing scheme avoid the overloaded region Moreover we propose a more realistic tra c model and show that our algorithm is valid even in such context At last we show that the algorithm is also e cient in a torus network These results show the relevance of this method