A Gossip-based Churn Estimator for Large Dynamic Networks

C. Giuffrida; S. Ortolani

A Gossip-based Churn Estimator for Large Dynamic Networks

C. Giuffrida
, S. Ortolani

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proceedings of the 16th Annual Conference of the Advanced School for Computing and Imaging
Publication status	Published - 2010

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title = "A Gossip-based Churn Estimator for Large Dynamic Networks",

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",

author = "C. Giuffrida and S. Ortolani",

year = "2010",

language = "English",

booktitle = "Proceedings of the 16th Annual Conference of the Advanced School for Computing and Imaging",

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TY - GEN

T1 - A Gossip-based Churn Estimator for Large Dynamic Networks

AU - Giuffrida, C.

AU - Ortolani, S.

PY - 2010

Y1 - 2010

N2 - 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

AB - 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

M3 - Conference contribution

BT - Proceedings of the 16th Annual Conference of the Advanced School for Computing and Imaging

ER -

A Gossip-based Churn Estimator for Large Dynamic Networks

Abstract

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