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Authors: Francisco Maia ; Miguel Matos and Fábio Coelho

Affiliation: INESC TEC & U. Minho, Portugal

ISBN: 978-989-758-182-3

Keyword(s): Large Scale, Data Stores, Epidemic Protocols.

Abstract: In the pursuit of highly available systems, storage systems began offering eventually consistent data models. These models are suitable for a number of applications but not applicable for all. In this paper we discuss a system that can offer a eventually consistent data model but can also, when needed, offer a strong consistent one.

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Paper citation in several formats:
Maia, F.; Matos, M. and Coelho, F. (2016). Towards Quantifiable Eventual Consistency.In Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016) ISBN 978-989-758-182-3, pages 368-370. DOI: 10.5220/0005929103680370

@conference{datadiversityconvergence16,
author={Francisco Maia. and Miguel Matos. and Fábio Coelho.},
title={Towards Quantifiable Eventual Consistency},
booktitle={Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016)},
year={2016},
pages={368-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005929103680370},
isbn={978-989-758-182-3},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Cloud Computing and Services Science - Volume 1: DataDiversityConvergence, (CLOSER 2016)
TI - Towards Quantifiable Eventual Consistency
SN - 978-989-758-182-3
AU - Maia, F.
AU - Matos, M.
AU - Coelho, F.
PY - 2016
SP - 368
EP - 370
DO - 10.5220/0005929103680370

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