Clustering Superpeers in P2P Networks by Growing Neural Gas

Dumitrescu, Mihai; Andonie, Răzvan

doi:10.1109/pdp.2012.73

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Since peer-to-peer networks are large and also dynamic, algorithms for managing them must be self-organized algorithms, and for this reason several self-organized algorithms such as Ant Colony (ACO), Growing Neural Gas (GNG), and the Schelling Segregation Model (SSM) have been reported for topology management in the literature [10,[18][19][20]. The SSM, which has been used as part of the proposed algorithm is briefly discussed in the rest of this paragraph.…”

Section: Introductionmentioning

confidence: 99%

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

Saghiri

Meybodi

2015

J Netw Syst Manage

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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.

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Section: Introductionmentioning

confidence: 99%