Small World Architecture For Peer-to-Peer Networks
Liu, Lu, Mackin, Stephen and Antonopoulos, Nick (2006) Small World Architecture For Peer-to-Peer Networks In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.
Small-world phenomenon has been observed in existing peer-to-peer (P2P) networks, such as Gnutella and Freenet. Due to the similarity of P2P networks to social networks, the previous small-world model proposed by Duncan Watts can be adopted in the design of P2P networks: each node is connected to some neighbouring nodes, and a group of nodes keep a small number of long links to randomly chosen distant nodes. Unfortunately, current unstructured search algorithms have difficulty distinguishing these random long-range shortcuts. This paper presents small world architecture for P2P networks (SWAN) with a semi-structured P2P search algorithm that is used to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Date :||1 December 2006|
|Identification Number :||10.1109/WI-IATW.2006.123|
|Additional Information :||In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops)(WI-IATW'06), pp. 451-454.© 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Depositing User :||Mr Adam Field|
|Date Deposited :||27 May 2010 14:46|
|Last Modified :||23 Sep 2013 18:36|
Actions (login required)
Downloads per month over past year