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

Abstract

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-toend delay and number of mismatched paths.

A self‐adaptive algorithm for super‐peer selection considering the mobility of peers in cognitive mobile peer‐to‐peer networks

“…In the recent years, LA has been used in different applications such as cognitive networks, 1 ad hoc networks, 13 wireless sensor networks, 14 WiMAX networks, 15 network security, 16 wireless mesh networks, 17 mobile video network surveillance, 18 vehicular environment, 19,20 P2P networks, 9,21,22 wireless data broadcasting systems, [23][24][25] smart grid systems, 26 grid computing, 27 and cloud computing, 28 to mention a few.…”

“…If a = b, the recurrence equations and are called linear reward penalty (L RP ) algorithm. More information can be found in Najim and Poznyak 11 : In the recent years, LA has been used in different applications such as cognitive networks, 1 ad hoc networks, 13 wireless sensor networks, 14 WiMAX networks, 15 network security, 16 wireless mesh networks, 17 mobile video network surveillance, 18 vehicular environment, 19,20 P2P networks, 9,21,22 wireless data broadcasting systems, 23–25 smart grid systems, 26 grid computing, 27 and cloud computing, 28 to mention a few.…”

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“…In the latter case, the action probability vectors remain unchanged when the action taken is penalized by the environment. Learning automata have a vast variety of applications in combinatorial optimization problems, [25][26][27][28] computer networks, 25,[29][30][31][32][33][34] queuing theory, 35 image processing, 36 information retrieval, 37,38 adaptive control, 39 neural network engineering, 40,41 cloud computing, 42 social networks, 24,[43][44][45] and pattern recognition. 46…”