Sciweavers

Share
OPODIS
2010

Biased Selection for Building Small-World Networks

8 years 3 months ago
Biased Selection for Building Small-World Networks
Abstract. Small-world networks are currently present in many distributed applications and can be built augmenting a base network with long-range links using a probability distribution. Currently available distributed algorithms to select these long-range neighbors are designed ad hoc for specific probability distributions. In this paper we propose a new algorithm called Biased Selection (BS) that, using a uniform sampling service (that could be implemented with, for instance, a gossip-based protocol), allows to select long-range neighbors with any arbitrary distribution in a distributed way. This algorithm is of iterative nature and has a parameter r that gives its number of iterations. We prove that the obtained sampling distribution converges to the desired distribution as r grows. Additionally, we obtain analytical bounds on the maximum relative error for a given value of this parameter r. Although the BS algorithm is proposed in this paper as a tool to sample nodes in a network, it...
Andrés Sevilla, Alberto Mozo, M. Araceli Lo
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where OPODIS
Authors Andrés Sevilla, Alberto Mozo, M. Araceli Lorenzo, José Luis López-Presa, Pilar Manzano, Antonio Fernández Anta
Comments (0)
books