July 7, 2010 to July 9, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISPDC.2010.25
Providing independent uniform samples from a system population poses considerable problems in highly dynamic settings, like P2P systems, where the number of participants and their unpredictable behavior (e.g., churn, crashes etc.) may introduce relevant bias. Current implementations of the Peer Sampling Service are designed to provide uniform samples only in static settings and do not consider that biased samples can directly affect the correctness of algorithms relying on a uniformity property or be exploited by a malicious adversary to increase the effectiveness of its attacks to the system. In this paper we provide a practical solution to the biasing problem by deploying a fully distributed Peer Sampling Correction Module on top of a given, possibly biased, peer sampling service. Samples provided by the peer sampling service will be locally processed by this module, using computationally efficient hashing functions, before getting to the application. The effectiveness of our approach is evaluated through an extensive simulation-based study.
Large Scale Distributed systems, Peer Sampling, Bias Correction
Roberto Baldoni, Marco Platania, Leonardo Querzoni, Sirio Scipioni, "Practical Uniform Peer Sampling under Churn", ISPDC, 2010, Parallel and Distributed Computing, International Symposium on, Parallel and Distributed Computing, International Symposium on 2010, pp. 93-100, doi:10.1109/ISPDC.2010.25