Robust lifetime measurement in large-scale P2P systems with non-stationary arrivals

Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work [26] has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary arrivals, which are often present in real systems, residual-based sampling does not correctly reconstruct user lifetimes and suffers a varying degree of bias, which in some cases makes estimation completely impossible. We overcome this problem using two contributions: a novel non-stationary ON/OFF churn model and an unbiased randomized residual sampling technique for measuring user lifetimes. The former allows correlation between ON/OFF periods of the same user and exhibits different join rates during the day. The latter spreads sampling points uniformly during the day and uses a novel estimator to reconstruct the underlying lifetime distribution. We finish the paper with experimental measurements of Gnutella and discussing reduction in overhead compared to direct sampling methods.