Effective Data Density Estimation in Ring-Based P2P Networks

Abstract

Estimating the global data distribution in Peer-to-Peer (P2P) networks is an important issue and has yet to be well addressed. It can benefit many P2P applications, such as load balancing analysis, query processing, and data mining. Inspired by the inversion method for random variate generation, in this paper we present a novel model named distribution-free data density estimation for dynamic ring-based P2P networks to achieve high estimation accuracy with low estimation cost regardless of distribution models of the underlying data. It generates random samples for any arbitrary distribution by sampling the global cumulative distribution function and is free from sampling bias. In P2P networks, the key idea for distribution-free estimation is to sample a small subset of peers for estimating the global data distribution over the data domain. Algorithms on computing and sampling the global cumulative distribution function based on which global data distribution is estimated are introduced with detailed theoretical analysis. Our extensive performance study confirms the effectiveness and efficiency of our methods in ring-based P2P networks.