Title
Performance Analysis of the Superpeer-based Two-layer P2P Overlay Network with the CBF Strategy
Abstract
Peer-to-peer (P2P) systems are widely used in various types of applications. In this paper, we evaluate the superpeer-based two-layer (SBTL) P2P overlay network with the charge-based flooding (CBF) algorithm to detect target peers which have target files, proposed as our previous work. The SBTL P2P overlay network is composed of two layers, normal peer and superpeer layers which include normal peers and superpeers, respectively. Multiple normal peers with some common properties, e.g. peers which have replicas of a file, are interconnected with a superpeer. A collection of a superpeer and normal peers is referred to as a cluster. In a cluster, a normal peer tries to find a target peer which has a target file by itself without help of its superpeer. If no target peer is detected in the cluster, the normal peer asks the super-peer to find the target peer. Then, the superpeer forwards the request message to other superpeers by using a type of flooding algorithm named the CBF algorithm at the super-peer layer. We evaluate the SBTL P2P model in terms of the number of messages exchanged among peers and communication load compared with other models.
Year
DOI
Venue
2007
10.1109/ICDCSW.2007.63
ICDCS Workshops
Keywords
Field
DocType
normal peer,cbf strategy,superpeer-based two-layer p2p overlay,target peer,superpeer layer,cbf algorithm,multiple normal peer,p2p overlay network,target file,charge-based flooding,sbtl p2p model,sbtl p2p overlay network,performance analysis,clustering algorithms,overlay network,p2p,message passing,bandwidth,application software,computer networks,information science
Computer science,Peer to peer computing,Computer network,Network performance analysis,Flooding algorithm,Message passing,Overlay network,Distributed computing
Conference
ISBN
Citations 
PageRank 
0-7695-2838-4
1
0.38
References 
Authors
10
3
Name
Order
Citations
PageRank
Kenichi Watanabe116015.86
naohiro hayashibara223836.23
Makoto Takizawa33180440.50