A Self-adaptive Algorithm for Topology Matching in Unstructured Peer-to-Peer Networks

作者：Saghiri Ali Mohammad; Meybodi Mohammad Reza^*

来源：Journal of Network and Systems Management, 2016, 24(2): 393-426.

DOI：10.1007/s10922-015-9353-9

• Mismatched paths
• Local search
• Local operator
• Schelling segregation model
• Learning automata

Peer-to-peer networks are overlay networks that are constructed over underlay networks. These networks can be structured or unstructured. In these networks, peers choose their neighbors without considering underlay positions, and therefore, the resultant overlay network may have a large number of mismatched paths. In a mismatched path, a message may meet an underlay position several times, which causes redundant network traffic and end-to-end delay. In some of the topology matching algorithms called the heuristic algorithms, each peer uses a local search operator for gathering information about the neighbors of that peer located in its neighborhood radius. In these algorithms, each peer also uses a local operator for changing the connections among the peers. These matching algorithms suffer from two problems; neither the neighborhood radius nor the local operator can adapt themselves to the dynamicity of the network. In this paper, a topology matching algorithm that uses learning automata to adapt the neighborhood radius and an adaptation mechanism inspired from the Schelling segregation model to manage the execution of the local operator is proposed. To evaluate the proposed algorithm, computer simulations were conducted and then the results were compared with the results obtained for other existing algorithms. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to end-to-end delay and number of mismatched paths.

• 出版日期2016-4