Model-driven Walks for Resource Discovery in Peer-to-Peer Networks

In this paper, a distributed and adaptive approach for resource discovery in peer-to-peer networks is presented. This approach is based on the mobile agent paradigm and the random walk technique with reinforcement learning to allow for dynamic and self-adaptive resource discovery. More precisely, this approach augments random walks with a reinforcement learning technique where mobile agents are backtracked over the walked path in the network. A metric recording an affinity value that incorporates knowledge from past and present searches is maintained between nodes. The affinity value is used during a search to influence the selection of the next hop. This approach is evaluated with the network simulator ns2.