Improving the Accuracy of Peer-to-Peer Sampling Services

Abstract

Node sampling services provide peers in a peer-to-peer system with a source of randomly chosen addresses of other nodes. Ideally, samples should be independent and uniform. The restrictions of a distributed environment, however, introduce various dependancies between samples. We review gossip-based sampling protocols proposed in previous work, and identify sources of inaccuracy. These include replicating the items from which samples are drawn, and imprecise management of the process of refreshing items. Based on this analysis, we propose a new protocol, Eddy, which seeks to minimize temporal and spatial dependancies between samples. We demonstrate that these changes lead to a better sampling service by showing, through simulations, that using Eddy improves the accuracy of a network-size estimation algorithm that uses the random samples from the protocol.