Name
Abstract | ||
|---|---|---|
Unstructured peer-to-peer (P2P) networks as Gnutella are dynamic, distributed systems without any centralizing point favoring failure tolerance and strength. However, resource search in these systems is an important problem. Gnutella's breadth-first search algorithm is flooding-based and generates a large amount of traffic thus making scalability difficult. This paper proposes a new search algorithm in which nodes, assisted by their local neuronal networks, selectively send the query to the most appropriate subsets of neighbors only. Hence, Gnutella algorithm is significantly improved and provides a greater percentage of findings with less amount of traffic generated on P2P network. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ITI.2009.5196154 | Dubrovnik |
Keywords | Field | DocType |
distributed processing,neural nets,peer-to-peer computing,tree searching,Gnutella algorithm,breadth-first search algorithm,distributed system,neural network,resources NeuroSearch,unstructured peer-to-peer network,Distributed Systems,Gnutella-like Systems,Neural Networks,Peer-to-Peer Networks,Resource Discovery | Search algorithm,Peer-to-peer,Computer science,Breadth-first search,Server,Computer network,Peer to peer computing,Artificial neural network,Scalability,Distributed computing | Conference |
ISSN | ISBN | Citations |
1330-1012 | 978-953-7138-15-8 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo Corbalán | 1 | 4 | 2.90 |
| Laura Lanzarini | 2 | 21 | 8.94 |
| Armando Giusti | 3 | 71 | 21.17 |