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

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.