A placement scheme for peer-to-peer networks based on principles from geometry

Author

Kleis, Michael ; Zhou, Xiaoming

Author_Institution

Competence Center Next Generation Network Integration, Fraunhofer Inst. FOKUS, Berlin, Germany

fYear

2004

fDate

25-27 Aug. 2004

Firstpage

134

Lastpage

141

Abstract

Crucial for the performance of peer-to-peer networks based on geometric topologies is the measurement complexity and quality of the mapping function used to map a node in the network to a point in the geometric target space. In this paper we study how results from mathematics as well as data mining can be applied to this mapping problem. Using a metric space model for networks and results from mathematics a relation between the number of nodes to be mapped, the worst case error of the mapping and the dimension of the geometric target space is formulated. As a main result geometric cluster placement (GCP) is presented, an improved and resilient placement algorithm based on GNP. An evaluation of GCP presented is based on measurement data from the RIPE NCC test traffic measurement (TTM) project.

Keywords

computational complexity; computational geometry; data mining; peer-to-peer computing; topology; RIPE NCC test traffic measurement project; data mining; geometric cluster placement; geometric target space; geometric topologies; geometrical principles; mapping function; measurement complexity; metric space model; node mapping; peer-to-peer networks; placement scheme; resilient placement algorithm; worst case error; Clustering algorithms; Data mining; Economic indicators; Extraterrestrial measurements; Geometry; Mathematical model; Mathematics; Network topology; Peer to peer computing; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Peer-to-Peer Computing, 2004. Proceedings. Proceedings. Fourth International Conference on

Print_ISBN

0-7695-2156-8

Type

conf

DOI

10.1109/PTP.2004.1334940

Filename

1334940