A Gossip-based Churn Estimator for Large Dynamic Networks

C. Giuffrida, S. Ortolani

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Gossip-based aggregation is an emerging paradigm to perform distributed computations and measurements in a large-scale setting. In this paper we explore the possibility of using gossip-based aggregation to estimate churn in arbitrarily large networks. To this end, we introduce a new model to compute local estimates and formally prove how aggregated values closely match the real churn with high accuracy independently of the network setting. Experimental results confirm the viability of our approach
Original languageEnglish
Title of host publicationProceedings of the 16th Annual Conference of the Advanced School for Computing and Imaging
Publication statusPublished - 2010

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