Towards Quantifiable Eventual Consistency

Francisco Maia, Miguel Matos, Fábio Coelho

2016

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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