An adaptive algorithm for managing gradient topology in peer-to-peer networks

Abstract

Super-peer network is a type of peer-to-peer networks. In a super-peer network, a super-peer is a peer that has more ability than other peers have and is responsible for some of the tasks of network management. Since different peers vary in terms of capability, selecting a super-peer is a challenge problem. Gradient topology is a type of super-peer networks. Because of dynamicity of peers, adaptive methods are important for managing gradient topology. A problem of the existing management algorithms of gradient topology is that they are not sensitive to joining and leaving the peers. This problem becomes more challenging when a malicious peer frequently joins and leaves the network. The proposed algorithm being sensitive to removal of super peers, using learning automata, selects the new super-peers in an adaptive manner. According to the simulation results, the proposed algorithm can compete with the existing algorithms.